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 …

Most graph neural network models learn embeddings of nodes in static attributed graphs for
predictive analysis. Recent attempts have been made to learn temporal proximity of the …

The widespread use of social networks enables the rapid diffusion of information, eg, news,
among users in very large communities. It is a substantial challenge to be able to observe …

Many relational data result from the aggregation of several individual behaviors described
by some characteristics. For instance, a bike-sharing system may be modeled as a graph …

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 anomaly detection plays a central role in many emerging network applications,
ranging from cloud intrusion detection to online payment fraud detection. It has been studied …

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 …

Network motifs provide a deep insight into the network functional abilities, and have proven
useful in various practical applications. Existing studies reveal that different definitions of …

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 …

Mining patterns in a dynamic attributed graph has received more and more attention
recently. However, it is a complex task because both graph topology and attributes values of …