1 research outputs found
Discovering Dense Correlated Subgraphs in Dynamic Networks
Given a dynamic network, where edges appear and disappear over time, we are
interested in finding sets of edges that have similar temporal behavior and
form a dense subgraph. Formally, we define the problem as the enumeration of
the maximal subgraphs that satisfy specific density and similarity thresholds.
To measure the similarity of the temporal behavior, we use the correlation
between the binary time series that represent the activity of the edges. For
the density, we study two variants based on the average degree. For these
problem variants we enumerate the maximal subgraphs and compute a compact
subset of subgraphs that have limited overlap. We propose an approximate
algorithm that scales well with the size of the network, while achieving a high
accuracy. We evaluate our framework on both real and synthetic datasets. The
results of the synthetic data demonstrate the high accuracy of the
approximation and show the scalability of the framework.Comment: Full version of the paper included in the proceedings of the PAKDD
2021 conferenc