Balance Maximization in Signed Networks via Edge Deletions

Abstract

In signed networks, each edge is labeled as either positive or negative. The edge sign captures the polarity of a relationship. Balance of signed networks is a well-studied property in graph theory. In a balanced (sub)graph, the vertices can be partitioned into two subsets with negative edges present only across the partitions. Balanced portions of a graph have been shown to increase coherence among its members and lead to better performance. While existing works have focused primarily on finding the largest balanced subgraph inside a graph, we study the network design problem of maximizing balance of a target community (subgraph). In particular, given a budget 𝑏 and a community of interest within the signed network, we aim to make the community as close to being balanced as possible by deleting up to 𝑏 edges. Besides establishing NP-hardness, we also show that the problem is non-monotone and non-submodular. To overcome these computational challenges, we propose heuristics based on the spectral relation of balance with the Laplacian spectrum of the network. Since the spectral approach lacks approximation guarantees, we further design a greedy algorithm, and its randomized version, with provable bounds on the approximation quality. The bounds are derived by exploiting pseudo-submodularity of the balance maximization function. Empirical evaluation on eight realworld signed networks establishes that the proposed algorithms are effective, efficient, and scalable to graphs with millions of edges.

Maximal Multipolarized Cliques Search in Signed Networks

“…In this section, we conduct extensive experiments to evaluate the performance of our proposed algorithms on the real-world signed The parameter of our algorithms is settled from the interval [3,7] with a default value of = 3; varies from the interval [3,7] with a default value of = 5. Unless otherwise specified, when a parameter is varying, another parameter is set to its default value.…”

“…However, these methods aim to find two communities in a global signed network. In the literature, considerable approaches [3,5,7,8] have been proposed for cohesive subgraph detection in signed networks. For example, the balanced clique model of signed networks is proposed in [3].…”

SSSNET: Semi-Supervised Signed Network Clustering

“…The standard heuristic for justifying the criteria for the embeddings hinges on the assumption that "an enemy's enemy is a friend" [53,10,17,34,25,26]. This heuristic is based on social balance theory [22,42], or multiplicative distrust propagation as in [20], which asserts that in a social network, in a triangle either all three nodes are friends, or two friends have a common enemy; otherwise it would be viewed as unbalanced. More generally, all cycles are assumed to prefer to contain either zero or an even number of negative edges.…”