Abstract: 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 window.However,existing graph generation methods suffer from some limitations.Most of them concentrate on either static community structures or temporal graphs without community structures,appearing weak in generating communities active during an anchor time period.To surmount their weakness,this paper introduces the concept of anchor community to depict frequent interactions between a group of nodes within an anchor time window.Then it proposes an algorithm to synthesize general temporal networks 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 distributions 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.

Key words: Temporal network, Anchor time window, Anchor community, Distribution probability generation model, Graph gene-ration