When the focus is on the relationships or interactions between entities, graphs offer an intuitive model for many real-world data. Such graphs are usually large and change over time, thus, requiring models and strategies that explore their evolution. We study the evolution of aggregate graphs and introduce the GraphTempo model that allows temporal and graph aggregation not only on node level by grouping individual nodes, but on a pattern level as well, where subgraphs are grouped together. Furthermore, we propose an efficient strategy for exploring the evolution of the graph based on identifying time intervals of significant growth, shrinkage, or stability. Finally, we evaluate the efficiency and effectiveness of the proposed approach using four real graphs.

Community detection in social networks is of great importance and is used in a variety of applications such as recommendation systems and targeted advertising. While detecting dense groups with high levels of connectivity and similar interests between their members is the main target of traditional network analysis, finding network members with quite different behavior than the majority of nodes is important as well. These nodes are known as outliers, and their accurate detection can be very useful; when outliers are marked as noisy nodes, their early exclusion from analysis can lead to high computational profits. On the other hand, they can represent interesting components that call for further investigation to find the reasons for their outlying behavior and possible ways to include them in a neighboring community. Both community and outlier detection are challenging in temporal environments where changes occur in real time; thus, dynamic methods need to be deployed rather than to static methods. In our work, we take into account the content of the network, in contrast to most of related studies, where only the network’s structure contributes to community formation. We define an adaptive outlier score to be assigned to each node in order to quantify its outlierness, and introduce a complete online community detection algorithm that analyzes both the network’s structure and content while at the same time detecting community outliers. To evaluate our method, we retrieved and processed two real datasets regarding social networks with temporal and content information. Experimental results show that our method is capable of detecting outliers in real-time evolving communities and provides an outlier score which is a better metric of each node’s outlierness compared to widely used metrics. Finally, experimental results indicate that our method is suitable for predicting the status of future nodes based on their current outlier score.