Xiaotian Zhuang | Searcharxiv

arXiv2024

Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation

Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges present within the constructed graphs. In this paper, we view these noisy edges as adversarial attack and propose to use a spectral adversarial robustness evaluation method to mitigate the impact of noisy edges on the performance of graph-based algorithms. Our method identifies the points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments demonstrate that our methodology is highly effective and outperforms state-of-the-art denoising methods by a large margin.

cs.LG↗

arXiv2024

Enhancing Graph Topology and Clustering Quality: A Modularity-Guided Approach

Current modularity-based community detection algorithms attempt to find cluster memberships that maximize modularity within a fixed graph topology. Diverging from this conventional approach, our work introduces a novel strategy that employs modularity to guide the enhancement of both graph topology and clustering quality through a maximization process. Specifically, we present a modularity-guided approach for learning sparse graphs with high modularity by iteratively pruning edges between distant clusters, informed by algorithmically generated clustering results. To validate the theoretical underpinnings of modularity, we designed experiments that establish a quantitative relationship between modularity and clustering quality. Extensive experiments conducted on various real-world datasets demonstrate that our method significantly outperforms state-of-the-art graph construction methods in terms of clustering accuracy. Moreover, when compared to these leading methods, our approach achieves up to a hundredfold increase in graph construction efficiency on large-scale datasets, illustrating its potential for broad application in complex network analysis.

physics.data-an↗