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

Many applications see huge demands of discovering important patterns in dynamic
attributed graph. In this paper, we introduce the problem of discovering trend sub-graphs in …

A great number of applications require to analyze a single attributed graph that changes
over time. This task is particularly complex because both graph structure and attributes …

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 …

We focus on the discovery of interesting patterns in dynamic attributed graphs. To this end,
we define the novel problem of mining cohesive co-evolution patterns. Briefly speaking …

A dynamic attributed graph is a graph that changes over time and where each vertex is
described using multiple continuous attributes. Such graphs are found in numerous …

Graph mining has numerous real-world applications. The goal is to extract interesting
subgraphs or patterns in graph databases. Dynamic attributed graphs are more complex …

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

To describe the dynamics taking place in networks that structurally change over time, we
propose an approach to search for attributes whose value changes impact the topology of …

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 …