符号网络上的强随机游走与弱随机游走
Strong and Weak Random Walks on Signed Networks
AI总结：
针对符号网络多社区弱平衡场景，本文提出弱随机游走并构建相似性矩阵，实验证明其在无监督与半监督聚类中优于强随机游走。
摘要：
随机游走在探测复杂网络结构方面发挥着重要作用。在传统网络上，它们可用于提取社区结构、理解节点中心性、执行链路预测或捕捉节点之间的相似性。在边权可为正或为负的符号网络上，设计一种可用于提取网络符号结构信息的随机游走并非易事，尤其是要具备将图划分为内部为正边、之间为负边的社区的能力。以往关于符号网络随机游走的研究集中于只有两个此类社区的情形（强平衡），而这在经验网络中很少见。本文提出一种符号网络随机游走，能够捕捉具有两个以上此类社区的网络结构（弱平衡）。该游走产生一个相似性矩阵，可用于将节点聚类为对立社区。我们从游走长度和平稳性方面比较了所谓的强随机游走与弱随机游走的特性。通过在合成网络和经验网络上的一系列实验，我们表明，基于弱游走的相似性矩阵既可用于无监督聚类，也可用于半监督聚类；当图具有两个以上社区，或表现出链路密度的不对称性时，其性能优于基于强游走的相同相似性矩阵。这些结果表明，只需用弱游走替代强游走来运行，其他基于随机游走的符号网络算法也可能得到改进。
英文摘要：
Random walks play an important role in probing the structure of complex networks. On traditional networks, they can be used to extract community structure, understand node centrality, perform link prediction, or capture the similarity between nodes. On signed networks, where the edge weights can be either positive or negative, it is non-trivial to design a random walk which can be used to extract information about the signed structure of the network, in particular the ability to partition the graph into communities with positive edges inside and negative edges in between. Prior works on signed network random walks focus on the case where there are only two such communities (strong balance), which is rarely the case in empirical networks. In this paper, we propose a signed network random walk which can capture the structure of a network with more than two such communities (weak balance). The walk results in a similarity matrix which can be used to cluster the nodes into antagonistic communities. We compare the characteristics of the so-called strong and weak random walks, in terms of walk length and stationarity. We show through a series of experiments on synthetic and empirical networks that the similarity matrix based on weak walks can be used for both unsupervised and semi-supervised clustering, outperforming the same similarity matrix based on strong walks when the graph has more than two communities, or exhibits asymmetry in the density of links. These results suggest that other random-walk based algorithms for signed networks could be improved simply by running them with weak walks instead of strong walks.
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。
