Christos Giatsidis

Evaluating Cooperation in Communities with the k-Core Structure

Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs -- such as DBLP and report interesting results.

Advanced graph mining for community evaluation in social networks and the web

Graphs constitute a dominant data structure and appear essentially in all forms of information. Examples are the Web graph, numerous social networks, protein interaction networks, terms dependency graphs and network topologies. The main features of these graphs are their huge volume and rate of change. Presumably, there is important hidden knowledge in the macroscopic topology and features of these graphs. A cornerstone issue here is the detection and evaluation of communities -- bearing multiple and diverse semantics. The tutorial reports the basic models of graph structures for undirected, directed and signed graphs and their properties. Next we offer a thorough review of fundamental methods for graph clustering and community detection, on both undirected and directed graphs. Then we survey community evaluation measures, including both the individual node based ones as well as those that take into account aggregate properties of communities. A special mention is made on approaches that capitalize on the concept of degeneracy (k-cores and extensions), as a novel means of community detection and evaluation. We justify the above foundational framework with applications on citation graphs, trust networks and protein graphs.

Quantifying trust dynamics in signed graphs, the S-Cores approach

Lately, there has been an increased interest in signed networks with applications in trust, security, or social computing. This paper focuses on the issue of defining models and metrics for reciprocity in signed graphs. In unsigned directed networks, reciprocity quantifies the predisposition of network members in creating mutual connections. On the other hand, this concept has not yet been investigated in the case of signed graphs. We capitalize on the graph degeneracy concept to identify subgraphs of the signed network in which reciprocity is more likely to occur. This enables us to assess reciprocity at a global level, rather than at an exclusively local one as in existing approaches. The large scale experiments we perform on real world data sets of trust networks lead to both interesting and intuitive results. We believe these reciprocity measures can be used in various social applications such as trust management, community detection and evaluation of individual nodes. The global reciprocity we define in this paper is closely correlated to the clustering structure of the graph, more than the local reciprocity as it is indicated by the experimental evaluation we conducted.