A Novel Graph Centrality Based Approach to Analyze Anomalous Nodes with Negative Behavior

AbstractDetection of different kinds of anomalous behaviors originating from negative ties among actors in online social networks is an unexplored area requiring extensive research. Due to increase in social crimes such as masquerading, bullying, etc., identification and analysis of these activities has become need of the hour. Approaches from two separate, yet, similar research areas, i.e. anomaly detection and negative tie analysis, can be clubbed together to identify negative anomalous nodes. Use of best measures from centrality based (negative ties) and structure based approaches (anomaly detection) can help us identify and analyze the negative ties more efficiently. A comparative analysis has been performed to detect the negative behaviors in online networks using different centrality measures and their relationship in curve fitting anomaly detection techniques. From results it is observed that curve fitting analysis of centrality measures relationship performs better than independent analysis of centrality measures for detecting negative anomalous nodes

Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network

Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actors’ centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered