基于相关Erdos-Renyi图度分布尾的无籽图匹配
Seedless Graph Matching via Tail of Degree Distribution for Correlated Erdos-Renyi Graphs
摘要：
网络对齐（或图匹配）问题是指恢复两个相关网络之间的节点到节点的对应关系。在本文中，我们提出了一种网络对齐算法，该算法不使用预先匹配的节点对的种子集或任何其他辅助信息（例如，节点或边标签）作为输入。该算法根据相邻节点经验度分布的尾部为节点分配结构创新特征。然后，根据这些特征进行节点匹配。我们评估所提出的算法在合成和真实网络上的性能。对于合成网络，我们生成的区域中的$Θ(\log(n)/n)$和$Θ(\log^{2}(n)/n)$，其中以前的工作理论上表明，恢复是可行的稀疏的埃尔多斯-雷尼图，当且仅当有一个边缘之间的一对节点之间的一个图形，也在其他图形中的相应节点的概率是在$Ω(\log(n)/n)$的顺序，其中$n$是节点的数量。在真实网络和合成网络上的实验表明，它在正确匹配概率方面优于以前的工作。
英文摘要：
The network alignment (or graph matching) problem refers to recovering the node-to-node correspondence between two correlated networks. In this paper, we propose a network alignment algorithm which works without using a seed set of pre-matched node pairs or any other auxiliary information (e.g., node or edge labels) as an input. The algorithm assigns structurally innovative features to nodes based on the tail of empirical degree distribution of their neighbor nodes. Then, it matches the nodes according to these features. We evaluate the performance of proposed algorithm on both synthetic and real networks. For synthetic networks, we generate Erdos-Renyi graphs in the regions of $Θ(\log(n)/n)$ and $Θ(\log^{2}(n)/n)$, where a previous work theoretically showed that recovering is feasible in sparse Erdos-Renyi graphs if and only if the probability of having an edge between a pair of nodes in one of the graphs and also between the corresponding nodes in the other graph is in the order of $Ω(\log(n)/n)$, where $n$ is the number of nodes. Experiments on both real and synthetic networks show that it outperforms previous works in terms of probability of correct matching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
