In dynamic networks, periodically occurring interactions express especially significant
meaning. However, these patterns also could occur infrequently, which is why it is difficult to …

In systems of interacting entities such as social networks, interactions that occur regularly
typically correspond to significant, yet often infrequent and hard to detect, interaction …

\emphSeasonal periodicity is a frequent phenomenon for social interactions in temporal
networks. A key property of this behavior is that it exhibits periodicity for multiple particular …

Graph data is found in numerous domains such as for the analysis of social networks,
sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to …

Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …

Graph mining has become a well-established discipline within the domain of data mining. It
has received much interest over the last decade as advances in computer hardware have …

Social interactions that occur regularly typically correspond to significant yet often infrequent
and hard to detect interaction patterns. To identify such regular behavior, we propose a new …

Many graphs in real-world applications, such as telecommunications networks, social-
interaction graphs and co-authorship graphs, contain temporal information. However …

Networks are used as highly expressive tools in different disciplines. In recent years, the
analysis and mining of temporal networks have attracted substantial attention. Frequent …

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a
significant role in many domains, such as social networks and bioinformatics. However, due …