On Cohesively Polarized Communities in Signed Networks
On Cohesively Polarized Communities in Signed Networks
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DOI：
10.1145/3543873.3587698
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Jason Niu;Ahmet Erdem Sarıyüce
中科院分区：
文献类型：
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作者：
Jason Niu;Ahmet Erdem Sarıyüce
定位和表征极化是实现更健康的网络生态系统的最重要问题之一。在这种情况下，找到形成强稳定协议并参与与其他群体的集体冲突的节点组是一个重要问题。以前的作品通过寻找平衡子图来解决这个问题，在平衡子图中，极性度量被优化，导致大子图没有明确的协议或冲突概念。在现实世界的符号网络中，平衡子图通常不像只有正边的子图那样是极化的。为了解决这个问题，我们利用内聚的概念-我们发现了一对内聚极化的社区，其中社区中的每个节点都与同一社区中的节点正连接，而与另一社区中的节点负连接。为了捕捉内聚沿着极化，我们定义了一个新的度量，二分法。我们利用平衡三角形，它模型的凝聚力和极化在同一时间，设计一个启发式的结果在现实世界的签署网络极化社区的良好温床。然后，我们介绍了电子分解，发现凝聚极化社区与高二分法得分。在广泛的实验评估中，我们表明，我们的方法发现凝聚力极化的社区，并优于国家的最先进的方法方面的几项措施。此外，我们的算法是更有效的比现有的方法和实用的大规模网络。
Locating and characterizing polarization is one of the most important issues to enable a healthier web ecosystem. Finding groups of nodes that form strongly stable agreements and participate in collective conflicts with other groups is an important problem in this context. Previous works approach this problem by finding balanced subgraphs, in which the polarity measure is optimized, that result in large subgraphs without a clear notion of agreement or conflict. In real-world signed networks, balanced subgraphs are often not polarized as in the case of a subgraph with only positive edges. To remedy this issue, we leverage the notion of cohesion — we find pairs of cohesively polarized communities where each node in a community is positively connected to nodes in the same community and negatively connected to nodes in the other community. To capture the cohesion along with the polarization, we define a new measure, dichotomy. We leverage the balanced triangles, which model the cohesion and polarization at the same time, to design a heuristic that results in good seedbeds for polarized communities in real-world signed networks. Then, we introduce the electron decomposition which finds cohesively polarized communities with high dichotomy score. In an extensive experimental evaluation, we show that our method finds cohesively polarized communities and outperforms the state-of-the-art methods with respect to several measures. Moreover, our algorithm is more efficient than the existing methods and practical for large-scale networks.