面向时序图数据的快速环枚举算法

时序图数据是一类边上带有时间戳信息的图数据.在时序图数据中，时序环是边满足时间戳递增约束的回路.时序环枚举在现实中有着很多应用，它可以帮助挖掘金融网络中的欺诈行为.此外，研究时序环的数量对于刻画不同时序图的特性也有重要作用.基于2018年由Rohit Kumar等人提出的时序环枚举算法（2SCENT算法），提出一种通过添加环路信息来削减搜索空间的新型时序环枚举算法.所提出的算法为一个两阶段的算法：1）首先，通过遍历原图获得所有可能会形成环路的节点，以及相应的时间和长度信息；2）然后，利用以上信息进行动态深度优先搜索，挖掘所有的满足约束条件的环.在4个不同的真实时序图数据集上进行了大规模的实验，并以2SCENT算法作为基准对算法进行了对比.实验结果表明，所提出的算法较之前最好的2SCENT算法要快50%以上.

Fast Temporal Cycle Enumeration Algorithm on Temporal Graphs

Temporal graph is a type of graph where each edge is associated with a timestamp. In temporal graphs, a temporal cycle denotes a loop where the timestamps of the edges in the loop follow an increasing order. Temporal cycle enumeration has a number of real-life applications. For example, it can be applied to detect the fraud behavior in temporal financial networks. Additionally, the number of temporal cycles can be used to characterize the topological properties of temporal graphs. Based on the 2SCENT algorithm proposed by Rohit Kumar et al. in 2018, a new temporal cycle enumeration algorithm is proposed which uses additional cycle information to prune the search space. Specifically, the proposed algorithm is a two-stage algorithm. First, the algorithm traverses the temporal graph to identify all root nodes that probably form temporal cycles, as well as the corresponding time and length information of the cycles. Second, the algorithm performs a dynamic depth first search using the above information to find all valid temporal cycles. Extensive experiments are conducted on four real-life datasets, using 2SCENT as the baseline algorithm. The result shows that the proposed algorithm reduces the running time over the 2SCENT algorithm by 50 percent.