Generation Algorithm of Temporal Networks with Anchor Communities
Algorithms for network analysis tasks require synthetic graph datasets to evaluate their effectiveness and efficiency.Real-world graph data not only possess topological features such as community structures,but also contain temporal information revealing evolutionary semantics.Nodes of real-world communities may interact with each other within a specific anchor time win-dow.However,existing graph generation methods suffer from some limitations.Most of them concentrate on either static commu-nity structures or temporal graphs without community structures,appearing weak in generating communities active during an an-chor time period.To surmount their weakness,this paper introduces the concept of anchor community to depict frequent interac-tions between a group of nodes within an anchor time window.Then it proposes an algorithm to synthesize general temporal net-works based on the distribution probability generation model,and further proposes an efficient generation algorithm of temporal networks with anchor communities(GTN-AC),allowing configuration input such as anchor time windows as well as specified dis-tributions of degree and timestamp.Extensive experimental results indicate that compared with other baseline methods,GTN-AC has a faster generation speed while ensuring preferable generation quality.
Temporal networkAnchor time windowAnchor communityDistribution probability generation modelGraph gene-ration
NETL
NSTL
万方数据
