CSNA 2012 - International Workshop on Complex Social Network Analysis (CSNA)

One of the reasons behind the tremendous success of Social Network Analysis (SNA) methods in various research disciplines is a very general and simple graph model that enables representation and study of extremely heterogeneous scenarios, ranging from workplace dynamics to the spreading of diseases or hyper-text documents in the World Wide Web. All these examples can be in fact modeled as homogeneous sets of nodes connected pairwise by some kind of edges. Commonly, each two nodes (social entities) are linked by a single edge (connection, link, bind, arc, tie), which, in turn, can be either undirected or directed, weighted or unweighted.
While this generality still constitutes a great value, recently, it has become apparent that to model specific contexts and to enable accurate analyses it may be important to enrich the simple network models with more complex modeling constructs. A typical example is a multi-layer (called also multi-layered, multi-modal or multidimensional) network, where nodes are connected to each other through different kinds of links and each such a kind corresponds to a single layer. Each layer can refer either to a different type of relationship (sociological approach: friendship / family ties / collaboration at work) or to a different type of user common activities (computer science approach: email exchange / commenting the same photo / usage of the same tags). Besides, in many domains and environments, we can extract more than one type of nodes - it means that a social network may be heterogeneous (multi-modal) rather than homogeneous. For example, we can have teacher-nodes (one type-mode) mixed with student-nodes (another type-mode) or we can distinguish different user accounts registered in a number of services, e.g. various online games. Both multi-layer(ed) and heterogeneous (multi-modal) networks can be generalized to simple networks extended with labels (types, classes, modes, layers, levels) assigned either to edges or nodes. This concept also covers spatial networks where nodes and edges are annotated (labeled) with their location. Temporal (dynamic) social networks expand this model by introduction of timestamps assigned to nodes or edges; its disparate feature is the fixed order among following snapshots.
Looking at current mainstream online social networks, we can easily see the relevance of these models: notable examples are Google+ circles, defining different kinds of social ties, users having multiple accounts in different web 2.0 services like Facebook, YouTube and Flickr, as well as Twitter conversations where messages are exchanged at precise timestamps and sometimes from the known locations. We refer to these enriched networks as complex social networks.
The objective of this workshop is to provide a venue to discuss the latest advances on complex social network analysis, mining and applications. Contributions coming from several fields, including computer science and social sciences, are welcome. The scope of the workshop includes (but is not limited to) the following topics:
Data models for complex social networks
Multi-layer(ed) networks
Multi-modal social networks
Multidimensional social networks
Spatial networks
Temporal analysis on social networks
Dynamics modeling and measures for social networks
Homogeneous social networks
Statistical modeling of complex social networks
Patterns in complex social networks
Complex social network mining
Cross-sectional analysis in social networks
Measures for complex social networks and algorithms for their calculation
Dynamics and evolution patterns of complex social networks
Algorithms for large-scale complex social networks
Merging social networks
User identification / unification in multiple-system social networks
Applications of complex social network analysis
Community discovery in complex social networks (multi-layered / multi-modal / heterogeneous communities)
Visual representation of complex networks and complex online social phenomena
Data protection in location-based networks
Methodological problems in complex social network studies
Data mining in social networking sites
Social Networks with Uncertainty
etc.