MODEC — Modeling and Detecting Evolutions of Communities

Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities

Community Evolution Mining in Dynamic Social Networks

Data that encompasses relationships is represented by a graph of interconnected nodes. Social network analysis is the study of such graphs which examines questions related to structures and patterns that can lead to the understanding of the data and predicting the trends of social networks. Static analysis, where the time of interaction is not considered (i.e., the network is frozen in time), misses the opportunity to capture the evolutionary patterns in dynamic networks. Specifically, detecting the community evolutions, the community structures that changes in time, provides insight into the underlying behaviour of the network. Recently, a number of researchers have started focusing on identifying critical events that characterize the evolution of communities in dynamic scenarios. In this paper, we present a framework for modeling and detecting community evolution in social networks, where a series of significant events is defined for each community. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. We also define the concept of meta community which is a series of similar communities captured in different timeframes and detected by our matching algorithm. We illustrate the capabilities and potential of our framework by applying it to two real datasets. Furthermore, the events detected by the framework is supplemented by extraction and investigation of the topics discovered for each community

Communities validity: methodical evaluation of community mining algorithms

Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data, that is represented in the form a graph wherein a link between two nodes indicates a relationship between them, there has been a considerable number of approaches proposed in recent years for mining communities in a given network. However, little work has been done on how to evaluate the community mining algorithms. The common practice is to evaluate the algorithms based on their performance on standard benchmarks for which we know the ground-truth. This technique is similar to external evaluation of attribute-based clustering methods. The other two well-studied clustering evaluation approaches are less explored in the community mining context; internal evaluation to statistically validate the clustering result and relative evaluation to compare alternative clustering results. These two approaches enable us to validate communities discovered in a real-world application, where the true community structure is hidden in the data. In this article, we investigate different clustering quality criteria applied for relative and internal evaluation of clustering data points with attributes and also different clustering agreement measures used for external evaluation and incorporate proper adaptations to make them applicable in the context of interrelated data. We further compare the performance of the proposed adapted criteria in evaluating community mining results in different settings through extensive set of experiments

Communities validity: methodical evaluation of community mining algorithms

Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data, that is represented in the form a graph wherein a link between two nodes indicates a relationship between them, there has been a considerable number of approaches proposed in recent years for mining communities in a given network. However, little work has been done on how to evaluate the community mining algorithms. The common practice is to evaluate the algorithms based on their performance on standard benchmarks for which we know the ground-truth. This technique is similar to external evaluation of attribute-based clustering methods. The other two well-studied clustering evaluation approaches are less explored in the community mining context; internal evaluation to statistically validate the clustering result and relative evaluation to compare alternative clustering results. These two approaches enable us to validate communities discovered in a real-world application, where the true community structure is hidden in the data. In this article, we investigate different clustering quality criteria applied for relative and internal evaluation of clustering data points with attributes and also different clustering agreement measures used for external evaluation and incorporate proper adaptations to make them applicable in the context of interrelated data. We further compare the performance of the proposed adapted criteria in evaluating community mining results in different settings through extensive set of experiments