Temporal Graph Generation Featuring Time-Bound Communities

Synthetic graph datasets are crucial for the assessment of network analysis algorithms, providing a measure of their effectiveness and efficiency. However, most existing generation techniques typically focus on community formation, neglecting the fact that real-world communities not only emerge but may also dissipate over time. This lifecycle occurs within a finite timeframe, adding complexity to the community dynamics. In this paper, firstly we introduce the concept of time-bound community to characterize communities that are destined to disintegrate. Secondly, we devise GTB, a temporal graph Generation method featuring Time-Bound communities, leveraging newly identified patterns within real datasets that we have collected. Additionally, we propose the advanced Temporal Edge Distribution (TED) model, a key component of G TB, designed for the swift generation of temporal edges within distribution limits and offering a constant time complexity to generate each edge. The TED model not only accommodates diverse distribution configurations but also facilitates its seamless transfer between time-bound communities to enhance time and space efficiency. Finally, extensive experimental results demonstrate that our method outperforms baseline methods by delivering generated content of superior Quality with notably competitive time and space consumption.