融合结构与属性相似性的加权图聚集算法

图聚集技术是将一个大规模图用简洁的小规模图来表示，同时保留原始图的结构和属性信息的技术。现有算法未同时考虑节点的属性信息与边的权重信息，导致图聚集后与原始图存在较大差异。因此,提出一种同时考虑节点属性信息与边权重信息的图聚集算法，使得聚集图既保留了节点属性相似度又保留了边权重信息。该算法首先定义了闭邻域结构相似度，通过一种剪枝策略来计算节点之间的结构相似度；其次使用最小哈希(MinHash)技术计算节点之间的属性相似度，并调节结构相似与属性相似所占的比例；最后，根据2方面相似度的大小对加权图进行聚集。实验表明了该算法可行且有效。

A weighted graph aggregation algorithm based on structural similarity and attribute similarity

Graph aggregation is a technology for representing a large scale graph with a concise graph that can preserve the structural and attribute information of the original large graph. Existing algorithms consider either the attribute information of nodes or the weight information of edges, and the difference between the original graph and the aggregated graph can thus be huge. So we propose a graph aggregation method considering both the attribute information of nodes and the weight information of edges, which enables the aggregated graph not only to preserve the similarity of node attributes but also edge weight information. Firstly, we define the closed neighborhood structural similarity, and use a structure pruning strategy to calculate the structural similarity between nodes. Secondly, minimum hash (Minhash) technique is employed to calculate the attribute similarity between nodes, and the proportions of structure similarity and attribute similarity are adjusted, based on which the weighted graph is aggregated. Experiments prove the feasibility and effectiveness of our method.