Programme

1.07.2016

09:00

Registration of the workshops' participants and morning coffee

Hall of the 1st floor

10:00

Workshops

Introduction to R and Social Network Analysis. Room 138 Intro Social : Teacher: Ju-Sung Lee

Network dependencies in social space, geographical space, and temporal space. Room 143 dencies in social space, geographical space, and temporal Teacher: Johan Koskinen

Analysing and visualizing texts and networks using R. Room 136...... Teacher: Wouter van Atteveldt

Multilevel ERGM analysis with MPNet. Room 142 .............................. .. Teachers: Julia Brennecke and Peng Wang

Semantic Network Analysis with Automap. Room 144 Teacher: Adina Teacher: Adina Nerghes

12:00

Coffee break

Corridor of the 1st floor

12:30

Workshops

Introduction to R and Social Network Analysis. Room 138 Intro Social : Teacher: Ju-Sung Lee

Network dependencies in social space, geographical space, and temporal space. Room 143 dencies in social space, geographical space, and temporal Teacher: Johan Koskinen

Analysing and visualizing texts and networks using R. Room 136...... Teacher: Wouter van Atteveldt

Multilevel ERGM analysis with MPNet. Room 142 .............................. .. Teachers: Julia Brennecke and Peng Wang

Semantic Network Analysis with Automap. Room 144 Teacher: Adina Teacher: Adina Nerghes

14:30

Lunch for workshop participants

Corridor of the 2nd floor

15:00

Registration and welcoming coffee

Hall of the 1st floor

15:30

Conference official opening

Keynote: Ronald Breiger. Conference hall

16:30

Coffee break

Corridor of the 1st floor

17:00

Parallel sessions

Qualitative analysis of multimodal networks. Room 136 иR oo m 1ь361 Chair: Ronald Breiger

Networks in Science, Technology, and Innovation 1. Room 138 Ch avccir Chair: Julia Brennecke

Words and networks 1. Room 141 Words and networks v1 . Rdovvvom 141 Co-chairs: Jana Diesner, Adina Nerghes

Statistical modeling of multimodal networks 1. Room 142 Chair: Peng Wang Chair: Peng Wang

2.07.2016

09:00

Registration and morning coffee

Hall of the 1st floor

10:00

Parallel sessions

Making Sense of Big Network Data: Testing Hypotheses on New Data 1. Testing Hypotheses on New Data 1. Room 136on New sss sdscccDt.a .... Chair: Iina Hellsten

Networks in Science, Technology, and Innovation 2. Room 138 Cha юir: Chair: Julia Brennecke

Words and networks 2. Room 141 Words and networks v1 . Rdovvvom 141 Co-chairs: Jana Diesner, Adina Nerghes

Statistical modeling of multimodal networks 2. Room 142 Chair: Peng Wang Chair: Peng Wang

Networks in Art: Practice and Structure, Meanings and Interactions 1. room Room 143 Ch xcxcxsdsddsdccxcxsdsco-chairs: dcxcco-co-cccchairsxavcxcx Co-chairs: Aleksandra Nenko, Dafne Muntanyola

12:00

Coffee break

Corridor of the 1st floor

12:30

Parallel sessions

Making Sense of Big Network Data: Testing Hypotheses on New Data 1. Testing Hypotheses on New Data 2. Room 136on New sss sdscccDt.a .... Chair: Iina Hellsten

Networks in Science, Technology, and Innovation 3. Room 138 Ch avccir Chair: Julia Brennecke

Words and networks 3. Room 141 Words and networks v1 . Rdovvvom 141 Co-chairs: Jana Diesner, Adina Nerghes

Networks in Art: Practice and Structure, Meanings and Interactions 1. room Room 143 Ch xcxcxsdsddsdccxcxsdsco-chairs: dcxcco-co-cccchairsxavcxcx Co-chairs: Aleksandra Nenko, Dafne Muntanyola

Socio-Material Network Analysis: Relating Individuals and Physical Contexts. Room 144Chair: Anisyafff KhokhlovaChair: Anisya Khokhlova Chair: Anisya Khokhlova

14:30

Lunch

Corridor of the 2nd floor

15:30

Keynote: Michael Batty

Conference hall

16:30

Coffee break

Corridor of the 1st floor

17:00

Parallel sessions and seminars

Social Movements and Collective Action as Network Phenomena. Afgj. Room 136 vements and Collective Action as Network Phenomena Afgj. Chair: Ioanna Ferra

Networked city: The multiplicity of urban links and nodes 1. Room 141 Chair: Michael Batty. Coordinator: Aleksandra Nenko

Seminar: Basic notions and measures of social network analysis in semantic network analysis. Room 142

Social media networks 1. Room 143 Social media networks 1. Room 143 Chair: Iina Hellsten

20:00

Conference reception

Kroo Cafe, Suvorovskiy pr., 27

3.07.2016

09:00

Registration and morning coffee

Hall of the 1st floor

10:00

Parallel sessions

Network analysis of cultural and social duality 1. Room 138 Chafdfdfdir: Chair: Ronald Breiger

Social media networks 2. Room 143 Social media networks 1. Room 143 Chair: Iina Hellsten

12:00

Coffee break

Corridor of the 1st floor

12:30

Parallel sessions

Network Analysis of Political and Policy-Making Domains. Room 136 т Chair: Artem Antoniuk

Network analysis of cultural and social duality 2. Room 138 Chafdfdfdir: Chair: Ronald Breiger

Networked city: The multiplicity of urban links and nodes 2. Room 141 Chair: Michael Batty. Coordinator: Aleksandra Nenko

Social media networks 3. Room 143 Social media networks 1. Room 143 Chair: Iina Hellsten

14:30

Lunch

Corridor of the 2nd floor

15:30

Conference official closing

Keynote: Peter Bearman. Conference hall
16:30

Break

Corridor of the 1st floor

17:00

Cultural programme

Sociological walk "Urban change and realms of memory". Guide: Anisya Guide: Anisya Khokhlova

Meeting place: Hall of the 1st floor

Conference abstracts

Sessions

Network analysis of cultural and social duality
Chair:
Ronald Breiger, University of Arizona

Invited speaker:
Jan Fuhse, Humboldt University of Berlin
 

In recent decades, the duality of culture and social structure as mutually constitutive has been in the focus of social science with a corresponding interest in symbols, meanings, texts, cultural frames, and cognitive schemas when studying social processes (Bourdieu 1984; Friedland and Alford 1991; Mohr 1998; Aleksander 2003). One stream of research applied network perspective to the level of institutions and/or fields, examining power control of social phenomena that involves cultural structures and social structures as conditioning symbolic ones (DiMaggio 1986; Mohr 1994; 2009). It has also been argued that the relations between the cultural and the social reveal themselves at the level of social (inter)action and practice, as individuals tend to switch between cultural classifications and social relations (White 1992), play on the gaps and contradictions in fields’ logics (Friedland and Alford 1991; Friedland and Roger 2009), and are guided by matters at hand (Bourdieu 1990) and by intersubjective relations (De Nooy 2003; Godart and White 2010). In those processes, meaning – which is then further integrated into cultural constructs and affects large-scale social structures - continuously emerges bottom-up.

Methodologically relevant are two-mode perspectives on meaning (Breiger 1974; 2000; Mohr 1994; 2000; Breiger and Mohr 2004) with their links to multimodal and multilevel data, such as in the socio-semantic approach (Roth 2013) and analytical techniques employing formal statistical modeling, including SAOMs and ERGMs. Another possibility is qualitative approaches - addressing the duality of structure and culture as meanings emerging from interaction, such as analysis of relational events (White 1992; Fuhse and Muetzel 2011) and sequences of events analysis (Bearman and Stovel 2000). There are also mixed methods using, for example, Galois lattices (Yeung 2005) or meaning contrasts analysis based on textual data

The session welcomes papers applying these or other network analysis methods to study the duality of culture and social structures either on the micro or the macro level, particularly welcoming papers addressing relations between the two levels.



Words and Networks
Co-chairs:
Jana Diesner, University of Illinois
Urbana-Champaign (UIUC)
Adina Nerghes, VU University Amsterdam

Invited speakers:
Adina Nerghes, VU University Amsterdam
Wouter van Atteveldt, VU University Amsterdam
 

This session is dedicated to cutting edge research at the nexus of text analysis and network analysis. While text analysis/ natural language processing and network analysis have evolved into mature yet quickly advancing fields, work at their intersection is less prevalent. Bridging this gap matters, since prior research has shown that without considering the content of text data, we are limited in our ability to understand the effects of language use in networks and vice versa. Jointly considering text data and network data enables the analysis of networks along multiple dimensions of human behavior, namely language use and social interactions, which ultimately helps to advance our understanding of the interplay and co-evolution of socio-technical networks and information. This conceptualization has inspired eminent work in areas such as: language change, the diffusion and adoption of information and beliefs online and offline, collective problem solving through information propagation, and relation extraction techniques.


To enable progress in this area, scholars have developed powerful and scalable methods and tools for analyzing text data authored or shared by network participants, as well as for language-based interactions. However, there is gap between theoretical foundations for these solutions and actual implementations in the form of empirical and computational work. For this session, we are looking for contributions that bridge the gap between theories related to language use, and/ or networks and methods/ tools for utilizing text data for network analysis. We are particularly interested in work that advances theories about the use or production of language or text data and integrates with network analytic methods and technologies.

Furthermore, we welcome methodological and theoretical contributions on the role and importance of context. The social context in which text is produced and consumed defines what topics and issues may be discussed, and to some extent, how these topics and issues are discussed. While text analysis and network analysis are versatile approaches, the socio-cultural context in which texts are produced may impact the degree to which meaningful information can be inferred. To this end, we are interested in work that addresses the social construction of meanings, the ways in which meanings are constrained by specific social contexts, and on text and network analytical methods adapted to capture these interactional aspects of text and meanings.

Another area of interest for this session is the conceptualization of network analysis metrics for the specific case of word networks. While it is common to apply social network analysis metrics to networks of words (or socio-semantic networks), very little effort has been dedicated to theorizing on how these metrics apply to networks in which the nodes are concepts or words. Arguably, a more wide-ranging conceptualization of network metrics for semantic networks would guide researchers in selecting those centrality metrics appropriate for their research goals and would support the inference of more robust interpretations of results. As such, we encourage contributions to the reconceptualization of network measures as tools of analysis in the specific case of word networks.



Socio-Material Network Analysis: Relating Individuals and Physical Contexts
Chair:
Anisya Khokhlova, St. Petersburg State University

Invited speaker:
Frédéric Godart, INSEAD, France
 

Social practices and encounters are always spatially patterned and subject to the powerful influence of the material contexts they are embedded in (Urry 1995). This involves multiple nonlinear effects as materiality both gives opportunities to, and puts constraints on, human activity. Things and physical spaces can constitute stimulating working environments, provide platforms for debates and cooperations, promote mutual learning of people, and provide cohesion of groups and organizations. At the same time, they can turn into objects of envy and competition and impact the (re)production of material and symbolic barriers between people and groups. Different objects are associated with different meanings, provoke various emotional responses and have different potential to generate motivation, trigger communication, and maintain identity and sociality. Network analysis can be useful to conceptualize how structures of physical contexts and social structures constitute each other. This session welcomes network-theoretical elaborations on the complex, dynamic processes in which human relations shape and are shaped by material contexts.

In the framework of the session we also propose to consider the methods of tracing cross-level links between individuals and material objects / spaces, as well as within-level relations between material objects and/or spaces themselves. One of the possible issues to discuss is the potential and limitations of sociological ethnography (e.g. qualitative observations and photo-elicitations) and interviews. Another issue is related to how qualitative methods can be effectively combined with quantitative ones, such as sociometric surveys. Finally, we suggest reflection on how the empirical data collected with mixed methods can be further subjected to formal analysis - jointly approaching material and social dimensions with such network analysis techniques as bipartite and multi-level network analysis, and open discussion of the methodological challenges in such complex research designs.



Statistical modeling of multimodal networks
Invited speaker and chair:
Peng Wang, Swinburne University of
Technology in Melbourne

Invited speaker:
Johan Koskinen, University of Manchester

 

Traditional network metrics describe the parameters of observed networks. Meanwhile, understanding of the processes that influenced the formation of an observed network structure requires statistical models that represent distributions of networks with similar structural features to those found in the observed network, hence inferring local network processes based on estimated parameter values.

By considering the interdependent nature of network links and the properties of the involved nodes, current statistical modeling techniques allow to account for different network configurations, as well as for nodal and dyadic level attributes, in order to determine the sets of factors that have a strong influence on the formation of an observed network. There are extensions for longitudinal data, revealing how the interplay of those factors unfolds in time. Moreover, the recent model developments for multi-partite networks comprised of links between ontologically different nodes, multiple networks consisting of multiple types of links among the same set of nodes, as well as multilevel networks combining multi-partite and unipartite networks, allows inferences on how these networks affect one another by simultaneously accounting for relations within and between networks of different kinds – inter-personal networks, semantic networks, organizational networks, material objects networks, networks of spaces, etc. For example, one can model how the usage of concepts connected in a semantic network is related to the existence of inter-personal ties. The models can also be compared to find differences and similarities in formation of networks in different cultures, societies, states, economies, organizations, cities, etc.

This session invites papers discussing methodological issues in multi-partite, multilevel and multiple statistical network models as well as papers testing hypotheses driven by developments in multimode network theory and applications along with the existing hypotheses tested on new data.

Particularly welcome are papers dealing with the possibilities of longitudinal multi-partite, multilevel and multiple network models.

Presentations of developments in relevant software would also be appreciated.



Qualitative analysis of multimodal networks
Chair:
Ronald Breiger, University of Arizona

Invited speaker:
Frédéric Godart, INSEAD, France
 

Currently, more and more qualitative researchers are joining the field of social network analysis, while quantitative network scholars increasingly also rely on qualitative approaches. Such a situation of cross-pollination can be particularly fruitful for the development of growing areas of network research, one of which is multimodal networks.

This session aims to facilitate contributions that use qualitative frameworks in addressing multimodal network data. One important issue is the collection and coding of data combining any types of interpersonal networks, semantic networks, cognitive networks, organizational networks, material objects networks, spaces and places networks, or other types of networks in diverse settings. Those settings may include a wide range of spheres, from science and technology or organizational and institutional contexts, to social movements, urban spaces, local communities, artistic groups, and others. The papers submitted to the session may discuss the potential in multimodal network data collection of ethnography-based methods and narrative inquiries, or combinations of those with more formal techniques such as network surveys or automated textual analysis. Other proposals are also most welcome. Especially interesting are techniques for longitudinal data collection and analysis.

Other pivotal issues are dealing with analytical techniques of qualitative network analysis that study how lines and nodes of ontologically different types are related – including multiple and multilevel perspectives – as well as strategies to compare networks across cultures, societies, states, economies, and cities. In this regard a possible question to address at the session is how network visualizations can be analyzed in a qualitative logic. Qualitative analysis techniques tackling the co-evolution of different types of networks are particularly welcome.

Theoretical grounding and methodology of multimodal data collection and analysis techniques can also provide the focus of papers submitted to this session.

Finally, papers dealing with the issues of mixing qualitative and quantitative analysis as well as the ways to integrate software in qualitative network research would be of interest.



Making Sense of Big Network Data: Testing Hypotheses on New Data
Chair:
Iina Hellsten, VU University Amsterdam
 

Traditionally, network data have been difficult to collect. Observation of network ties among people is time-consuming and therefore restricted to ties within small groups. There are limits to the number of network contacts a respondent is able and willing to list in a questionnaire (Bernard et al. 1990) and surveying the ties of and among network contacts requires snowballing samples that increase quickly in size (Laumann, Marsden & Prensky 1983). It has long been recognized that networks can be constructed from archival records (Burt & Lin 1977, Burt 1983), but the advent of large-scale digital data storage is now offering access to network data that are nearly as limitless as social networks themselves. The Internet, as a network of pages linked by hyperlinks, logs of contacts between persons through (mobile) phones, social networking sites, and so on, links between people and products from purchases with credit cards or customer cards, geographical proximity between persons based on GPS tracking; relational data suddenly seem to be generated everywhere.

The sheer size of the data poses technical problems, however, these are not central to the session. Instead, we invite contributors to focus on the substantive questions that can be answered with these types of data and the methods needed to find answers. We welcome proposals and examples of ways in which (long-)standing hypotheses from the social and behavioral sciences may be put to new and perhaps better tests using Big Network Data. For example, availability of data on the overall structure of the Internet allowed a new test (Barabási & Réka 1999) of the Matthew Effect (Merton 1968) or Cumulative Advantage Processes (De Solla Price 1976), which is now commonly known as Preferential Attachment. In addition to analyzing the overall structure of a network, large-scale network data may also serve as a sampling frame for clever selection of ego-networks or pairs allowing tests of network hypotheses such as a tendency for transitive closure of social contacts (Kossinets & Watts 2006). Novel approaches to theoretically driven network questions are likely to emerge, and we welcome papers introducing them in traditional fields, as well as emerging research topics such as innovation ecosystems, intellectual landscapes, and network cultures, to name a few.

Particularly welcome will be papers (1) devoted to big data on relations between networks of different kinds – inter-personal, semantic, organizational, material objects, etc., - (2) applying to big data approaches that combine several networks, including multilevel, multiple or other combinations, as well as (3) using big data to compare networks across cultures, societies, states, economies, cities, - and so on.



Networks in Science, Technology, and Innovation
Invited speaker and chair:
Julia Brennecke, Swinburne University of
Technology in Melbourne
 

Systems of relations – and thus networks – can be considered as drivers of science, technology, and innovative change. Well-known examples of successful science, technology and innovation in networks originated in the electronics industries of Silicon Valley in California and Route 128 in Massachusetts; machine-tool, apparel and tile industries in northern Italy; the machine-tools and automotive industries of southern Germany; and others. Experience of the innovation leaders has shown that multiple relational structures between organizations and individuals can provide high levels of diversity, and can be considered as the most favourable condition for knowledge creation, as well as for the emergence and development of innovations. Networks allow their members to get access to diverse information and competencies, enhance organizational learning capabilities, reduce costs, and minimise risks (De Man 2008; Malerba 2009; Johnson 2009). Social network analysis allows to gain an understanding of how the ties allowing collaborative research, technological breakthroughs, and innovation emerge and develop (Rogers 1962; Valente 1995; Powell et al. 1996; Pyka & Küppers 2002; Burt 2005; Tortoriello & Krackhardt 2010). We invite well-grounded contributions based on the traditional social-network perspective that introduce new methods and techniques drawing on the established operationalizations.

Whereas social network analysis has mainly focused on social agents and their relations (Freeman 2004), the network approach to science, technology, and innovation studies (STI) has been multimodal from the beginning. In science studies, “networks of scientific papers” (Price 1965) are organized intellectually at the above-individual level into journals and disciplines; in technology studies, “selection environments” (Nelson & Winter 1982) induce technological trajectories and regimes; and in innovation studies, the focus is on the non-linear interfaces between R&D and markets.Can the transversal dynamics of innovation be modeled using network analysis? Multimodal networks of what components should be analyzed: words, people, or perhaps ideas? What is evolving in the dynamics of these networks: industries, routines, technologies, markets? How can these multimodal networks be operationalized, both in terms of their complexity as interfaces at each moment of time, and in terms of changing priorities over time? How are the emerging developments socially constructed, and how do the constructs feed back on the constructors themselves? What is the role of latent constructs such as regimes and disciplines? Papers dealing with these issues are particularly welcome.



Social media networks
Invited speaker and chair:
Iina Hellsten, VU University Amsterdam
 

The rise of social media enables online social networks. Social media has been considered as a virtual space for social interactions, but specific social media also has its particular technical functions that may facilitate or block certain types of uses of the ‘space’. We approach the concept of social space as a metaphor for a new type of place for social interactions, the main characteristics being the flexibility of boundaries between social groups with unclear group membership. This poses challenges for research into the social and semantic networks in the various types of social media, such as online communities, blogs, Facebook, YouTube, Twitter and other social media forums. This new social media data enables more varied approaches that combine social, content and meanings networks online. Such multimode networks pose new challenges for social sciences theories and methods.

This has also lead to more and more data being collected about us and our activities. Our social media interactions and our online search and shopping behaviour, but also offline location tracking and sensor and surveillance networks are all generating large, often real-time data sets. Potentially, these could help answer important questions facing society today. But processing them productively represents a real challenge, at the interface of social and computer science. Meeting that challenge requires not only thorough comprehension of the methodologies involved in analysing big data, but also a theoretical understanding of societal problems and, not least, the associated legal and ethical factors.

The session on social media networks focuses on the theoretical and methodological challenges posed by social media interactions.

In particular, we invite presentations that combine social network measures with semantic networks, and focus on multi-mode networks of actors and the content of their communications.



Networks in Art: Practice and Structure, Meanings and Interactions
Co-chairs:
Aleksandra Nenko, NRU ITMO
Dafne Muntanyola, Autonomous University
of Barcelona
 

From a pragmatic point of view, art is a collective act opposed to individual practice. The social nature of art has been addressed from different standpoints: from revealing how new styles in art emerge due to changes in its institutional structure (White & White 1965), to showing power relations in the art field (Bourdieu 1983). Also, by defining the conventionally organized operations in the art world (Becker 1983) and arguing the importance of everyday interactions and friendly dynamics in creative collaborative circles (Farrell, 2003, Collins, 2015). Network analysis is a powerful tool to capture the multiplicity of these relations in art.

Network analysis has been extensively used to develop Bourdieu’s and Becker’s elaborations on the social structure of art: network effects of positions were found in the space of cultural production (Muntanyola & Lozares, 2006; Crossley 2009; Bottero and Crossley 2011); formation of evaluative categories and artistic judgements have been studied (de Nooy 2009); artistic careers have been taken as trajectories through professional institutions and events (Giuffre 1999, Moureau & Zenou, 2014); and creative performance has been shown as an effect of the small world structure (Uzzi & Spiro 2005). The session welcomes papers continuing the work from within these established frameworks.

Network analysis also allows to empirically locate and relate artistic practices and operationalize concepts of focused interaction (Goffman 1961), artful practices (Garfinkel 1967), and habitus (Bourdieu 1980; Wacquant 2015). Applied to textual data network analysis, it provides tools to study how meanings emerge from interactions between artists. Network analysis can be used to capture the material embeddedness of artistic practice in the immediate settings of studios (Farias & Wilkie 2016), local buzz (Currid & Williams 2009) and networked complexity of a ‘creative’ city (Comunian 2011). Network analysis can also capture the positions of artworks in structures of relations between actors, while the former are collaboratively created through embodied and materially embedded communication (Albertsen & Diken 2004). Studying the composition of art networks in connection to public participation reveals possibilities for social empowerment through art, and shows ways to implement principles of relational aesthetics (Bourriaud 1998). We welcome papers that adopt network analysis to study the variety of phenomena in art including those proposing alternative perspectives.

This is a thematic session, so the methodological options are multiple. Particularly encouraged are papers on methodology and applications of multimodal network analysis, including those studying the duality - for example, of places / events and artists, social and cultural structures, actors and objects - in artistic settings. This session will put together papers that are empirically sound with the detailed interactions that make the rehearsal a material context for action..We also expect papers on both micro and macro scale studies of social relations between individuals, institutions or meanings.



Networked city: The multiplicity of urban links and nodes
Chair:
Michael Batty, University College London

Coordinator:
Aleksandra Nenko, NRU ITMO
 

Since Lefebvre 2003 [1970] announced ‘planetary’ urbanization, researchers have been calling for new paradigms in theory and methodology to grasp the city's complexity. However, the apt remark by Soja (2000: xii) remains true ‘it may indeed be both the best of times and the worst of times to be studying cities’ because of the ‘restless periodicity and extraordinary slipperiness of the urban phenomenon itself’ (Brenner et al. 2011 : 226).


The concept of ‘networks’ has become a new metaphor to thinking the city complexity, and is elaborated in the framework of two influential conglomerates of research. First is represented by works of M. Batty (2005; 2013) who considers urban dynamics in the context of complexity theory and models myriads of processes and elements that combine into organic wholes. Elaborating urban morphology and patternology, Batty shows different kinds of networks as structural under-layers of multiscale urban dynamics.

Second is assemblage theory and actor-network theory which incline thorough investigation of sociomaterial configurations and non-human agencies in cities. Applications of this approach in urban research were presented by Farías and Bender’s volume (2010). Following this strand of thinking McFarlane (2011) describes the city through ‘grammars of gathering, networking and composition’ of different agents (p. 207). The assemblage approach to cities is criticized for lack of analytical and critical power by Brenner et al. (2011) however is advocated further by Farias (2011).

Parallel and in connection to these two meta-narratives, there are multiple applications and achievements of network analysis in considerations of the city, summed up, for example, by Neal (2013) who defines three levels of network phenomena: networks of urban communities, the city as network and networks of cities.

Network analysis allows researchers to explain the relations between diverse networks in the urban environment - from interpersonal to technical ones - influencing each other. There are numerous examples of such relations and influences between urban networks. To name a few, those are segregation and cohesion in urban communities as a consequence of spatial structure of the city streets and meeting spots, alternative centers of urban life based on clustering of urban practices represented in social media, e-neighborhoods and virtual spatial communities formed via Internet, paths and landmarks determining perception of city environment with its flows and the dynamics of city life.

We invite both papers that present comprehensive elaboration of theoretical assumptions and pick up networks as ontologies and those using network analysis methodologies to address the complexity of urban phenomena in European urban landscapes and beyond.



Social Movements and Collective Action as Network Phenomena
Chair: Ioanna Ferra, University of Leicesster  

The role of social networks in collective action processes has been analyzed in the last few decades from a plurality of perspectives. A substantive bulk of research has been devoted to the mechanisms behind individual decisions to become – and remain – committed to the pursuit of collective goods. Whether the focus was on employees organizing to demand better working conditions, residents joining together to clean their neighborhood streets, or radical activists committing to a long-term project of social transformation, how embeddedness in networks affects people’s decisions to engage in collective action has attracted considerable attention (Diani 2004). At the same time, research has also mapped the structure of the inter-organizational exchanges that develop between groups and organizations interested in similar issues, whether in the form of ad hoc coalitions or fully-fledged social movements (Diani and Bison 2004).

Although most network research on collective action has been conducted by social movement scholars (Diani and McAdam 2003), the two dimensions do not necessarily overlap, from theoretical and methodological perspectives. This session invites submissions from scholars interested in collective action at large, i.e., in the joint pursuit of collective goals by individuals and/or organizations, whether of the radical or the moderate type, and regardless of whether the focus is on protest activity or service delivery as in the case of volunteering (Wilson 2000). Submissions focusing on quantitative or qualitative approaches to social network analysis are both welcome. So are studies of ego-networks as well as explorations of complete networks. We also encourage proposals from scholars investigating collective action through virtual online connections, such as social networking sites (Ackland and O’Neil 2011; Gonzalez-Bailon 2009), the spatial dimension of social networks (Nicholls, Miller, and Beaumont 2013), and the time evolution of collective action networks (Mische 2008).

With regard to the 2016 NetGloW topic, we particularly welcome papers using multimodal, multilevel, or multiple network perspectives (for example, analysis of networks including relations between organizations and individual actors) - as proposed by Mario Diani in his keynote talk at NetGloW’14 - and/or papers which compare social movements networks / collective action networks across different cultural and social settings of European societies and beyond.



Network Analysis of Political and Policy-Making Domains
Chair: Artem Antoniuk, St. Petersburg State University

 

The study of policy and political networks has a distinguished history in the social sciences. David Knoke (1990) identified the theoretical objectives of the field as ranging from explaining and predicting collective policy decisions and outcomes, to exploring how networks form, persist, and change over time. The study of political networks also engages with theories of political influence, aiming to explain how relations among actors can affect their political identity and behavior. The thematic scope of the session includes studies of policy-making and political processes through the network perspective, focusing on relational structures and interactions between governmental and nongovernmental organizations, interest groups, and individuals. From a methodological perspective, such research requires the collection of attribute data about actors, and of relational data determining the ties between them. It is also important to have institutional and social data to contextualize the political framework within which political decisions are made.

The session welcomes papers focusing on, but not limited to, the following aspects of political and policy networks:relations between actors shaping their political attitudes, preferences, and opinions; implications of network structures for actors engaged in contesting and collaborating within specific public policy arenas; cross-country similarities and/or differences in the structure of policy networks; policy network change across different stages of the policy cycle; key players within policy space and privileged network positions, such as brokerage and centrality, reflecting actor political power.

There is a range of open questions emerging from active – and thus to an extent asynchronous – development of theory and methodology of political networks analysis, which paper authors are particularly welcome to address within this session. One of them concerns the evaluation of political weight and power of structurally central actors and groups in the network. This objective related to power and influence theories requires further development of the methodological network tools. Do the combination of network data and survey data, or rankings of influential actors made by magazines, provide sufficient attribute data for modeling power distribution in the network? Is it possible to test hypotheses about the interdependence between the relational structure as a variable that determines, or is affected by, the individual characteristics of specific political actors?

Among other widely discussed issues is the question of how multimodal data on policy networks may provide substantive insights into policy analysis. The main methodological problem concerns processing of multimodal network data and choice of appropriate network measures. The theoretical problems are the interpretation of the outcomes of multimodal data analysis in relation to the field of policy network research, and possible applications within other domains of political studies. The session particularly welcomes papers dealing with these problems as well as applications addressing topical issues of European societies.

Seminars

Basic notions and measures of social network analysis in semantic networks
Moderator:
Adina Nerghes, VU University Amsterdam
 

Topics of discussion:


Range of relations:


The existence of links in semantic networks based on word co-occurences is defined by window size - the range in text (number of words) within which words are considered to be linked. This range is set by an analyst and affects the structural properties of the resulting network significantly. Like in social networks, the total structure influences the network measures and their interpretation. For example, betweenness centrality is affected by the total density of a network. It is more or less clear in social networks, where a tie is considered existing at a certain level of information exchange or interaction frequency. Yet, it is more difficult to decide whether words, for instance, in a range of 2 or those in a range of 4 from each other should be considered as linked. How should one choose a window size with regard to the affect on the network measures it makes?

Binary vs. valued:


Semantic networks can be unweighted as well as weighted, just like social networks. While networks with unweighted links are easier to compare, networks with weighted links allow the researcher to retain more information. Because the links in most semantic networks are based on co-occurrences, an unweighted link in a semantic network normally represents that two words co-occurred in the specified window (existence of a relation), while a weighted link also shows how often two words co-occurred within the specified window across the corpora (the intensity of that relation). By contrast to social networks, weighted semantic networks expose the emphasis that is placed on the relationships between two nodes (the concepts in an underlying text). Such valence becomes key when attempting to identify certain linguistic strategies, such as frames or metaphors. For example, a strong link (read as high value link) between two concepts in a semantic network may indicate the presence of an n-gram. An n-gram may be indicative of a commonly used expression but it can also indicate a highly institutionalized and accepted frame (e.g. financial crisis). Therefore, the measures accounting for link weight must have a different mean in semantic networks.


Directionality:


Like in most of the social networks, the links in semantic networks can be extracted either as undirectional or as directional. Yet, in semantic networks directional links are based on the direction of word associations. So if in a social network we can talk about ‘flows’ or (un)reciprocal relations between entities, in semantic networks grammatical and syntactic structures are behind directionality of links. While some authors have argued in favor of this directional approach, others maintain that the inherent meaning of texts is undirected. Thus, the question arising here is whether or not directionality of semantic network relations is of importance at all. If it is, the difference in meaning of such social network measures as in- and out- degree in semantic networks should be discussed.

Software workshops

Introduction to R and Social Network Analysis
Teacher:
Ju-Sung Lee, Erasmus University Rotterdam
 

In this hands-on workshop, participants will learn the fundamentals of R, the popular, open source statistical software, and social network analysis through R. We will cover topics such as data input/output, data manipulation (including an overview of data types, mathematical operators and functions), control statements (e.g., conditionals and loops), writing your own custom functions, and basic data visualization.

Next, basic statistical analysis will be introduced including statistical tests such as the correlation, t-test, ANOVA, and multiple linear regression.

Finally, we will use network add-on packages for R, namely 'sna', 'statnet', and 'igraph', to analyze and visualize some sample network data. Analysis will include centrality measures, community detection, hypothesis testing, and network models, i.e. multiple regression QAP (MRQAP) and exponential random graph models (ERGMs). Participants are encouraged to bring their own data, but sample data will be provided to those who do not have their own data.

Target audience: This workshop is appropriate for those with little or no experience in R or social network analysis. Some programming (R or otherwise) and/or SNA experience would be helpful.

Requirements: Participants are required to bring their own laptops with R and optionally RStudio installed. Mac OSX users will require XQuartz installed (if not already included with the OS) for network and data visualization.

The workshop duration is five hours.



Network dependencies in social space, geographical space, and temporal space
Teacher:
Johan Koskinen, University of Manchester
 

Statistical analysis of social network data is becoming increasingly popular and is progressively adding new substantive insights to the literature. The intricate contingencies of social relations in networks that have been the key focus of network research – such as triadic closure – are also what make statistical analysis of networks more involved than standard statistical analysis. In particular, these contingencies vitiate standard assumptions of independent observations. However, the explicit modelling of these dependencies is the express purpose of some statistical models for networks. Here we deal with two such modelling frameworks. This workshop will introduce exponential random graph models (ERGM) and stochastic actor-oriented models (SAOM) for analysing network data.

A general introduction to statistical modelling of ties in a network will be presented and the use of ERGM and SAOM will be exemplified with application of these models to a number of example data sets. These quick hands-on demonstrations will use MPNet, and the R-packages statnet and RSiena, and to get the most out of these exercises it is recommended that you bring your own laptop with these programmes installed. The overall aim is to provide a overview and working handle on how the principled consideration of how the basic interdependencies of relational ties extends to physical and temporal space. Some basic familiarity with social network analysis will be helpful.

MPNet program and manual: http://sna.unimelb.edu.au/PNet

Statnet tutorial: www.jstatsoft.org/article/view/v024i09/v24i09.pdf

Comprehensive RSiena manual (and additional resources): https://www.stats.ox.ac.uk/~snijders/siena/

A useful tutorial on SNA in R: http://www.bojanorama.pl/snar:start



Semantic Network Analysis with Automap
Teacher:
Adina Nerghes, VU University Amsterdam
 

This hands-on workshop will introduce methods and applications bridging text analysis and network analysis with Automap. Participants will learn how to conduct data analysis at the nexus of these areas in an informed, systematic and efficient manner.

First, the workshop will cover pre-processors for cleaning raw text collections. Through various processors available in Automap, participants will learn how to generate and apply delete lists, remove noise words etc. During this step, we will also cover basic content analysis methods available in Automap (e.g., concept frequency, co-occurrence lists, key words in context etc)

Secondly, participants will learn how to generate semantic networks from collections of texts, the various choices and methods of network generation.

Thirdly, we will cover the development of thesauri and ontologies to extract social structure existent in text documents. Such information as named entities, locations or events is often available as text data, and can serve as a single or complementary source of information about networks.

Some of the techniques for pre-processing Natural Language that will be covered during the workshop are: Named-Entity Recognition; Stemming (Porter, KStem); Bigram and N-gram Detection; Extraction routines for dates, events, parts of speech; Deletion and delete lists; Thesaurus development and application; Flexible ontology usage; Parts of Speech Tagging.

Target audience: The workshop is targeted especially towards those with limited or moderate experience with text analysis and semantic networks.

Requirements: Participants are required to bring laptops with Automap installed (http://www.casos.cs.cmu.edu/projects/automap/software.php). Automap is not compatible with Mac OS but Mac users can make use of a virtual machine environment (e.g., Parallels, VirtualBox, Bootcamp etc.).

The workshop duration is five hours.



Analysing and visualizing texts and networks using R
Teacher:
Wouter van Atteveldt, VU University Amsterdam
 

R is a very powerful and flexible statistics package and programming language, which also has a number of packages for text and network analysis. In this workshop you will learn how to use R to and do corpus analysis to analyse and visualize large text collections; build semantic networks from text; and visualize and analyse networks. The workshop will be a mix of interactive lectures and individual practice using handouts, we will provide data for you but you can also use your own data if you bring e.g. the raw texts in .txt format.


Prior knowledge of R is not strictly required. However, please make sure that you install R and Rstudio on your laptop beforehand. If you have never used R you are also advised to play around with R a little bit to make sure you are not overwhelmed at the workshop. You can have a look at the Coursera course on R (https://www.coursera.org/learn/r-programming) and/or the tutorials for learning r (http://vanatteveldt.com/learningr).



Multilevel ERGM analysis with MPNet
Teachers:
Julia Brennecke and Peng Wang,
Swinburne University of
Technology in Melbourne
 

In this hands-on workshop, participants will learn the fundamentals of estimating Exponential Random Graph Models (ERGMs) with MPNet – a software developed to investigate the structural features of networks. The workshop will start with a brief introduction to the overall logic of estimating (single-level) ERGMs before introducing the recently developed multilevel ERGMs. The latter class of models enables researchers to investigate the influence of structure at one level of analysis on structure at a different level, while taking into account the complex interdependencies that exist within and between levels. For instance, interpersonal networks between managers at the micro-level might interact with alliance networks of the organizations they are nested in.

The workshop will start with a brief introduction to the overall logic of estimating (single-level) ERGMs before introducing the recently developed multilevel ERGMs. The latter class of models enables researchers to investigate the influence of structure at one level of analysis on structure at a different level, while taking into account the complex interdependencies that exist within and between levels. For instance, interpersonal networks between managers at the micro-level might interact with alliance networks of the organizations they are nested in.

Throughout the workshop, participants will work through short exercises to get familiar with the graphical user interface and output of the MPNet software. Moreover, we will discuss various case-study examples that will provide the participants with a good understanding of the possibilities that multilevel ERGMs offer for social scientists.

Requirements: Some basic familiarity with social network analysis will be helpful. Participants are required to bring their own laptops with MPNet installed. Note that MPNet is not compatible with Mac OS without a compatible Windows parallel.

MPNet program and manual: http://www.swin.edu.au/melnet