arXiv subjects

Muhieddine Shebaro

Publications and source records attributed to Muhieddine Shebaro.

4 recordsLinked to original sources

arXiv2025

KAN KAN Buff Signed Graph Neural Networks?

Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neural networks to model complex graph structures. Recently, the Kolmogorov-Arnold Neural Network (KAN) has emerged as a promising alternative to the traditional Multilayer Perceptron (MLP), offering improved accuracy and interpretability with fewer parameters. In this paper, we propose the integration of KANs into Signed Graph Convolutional Networks (SGCNs), leading to the development of KAN-enhanced SGCNs (KASGCN). We evaluate KASGCN on tasks such as signed community detection and link sign prediction to improve embedding quality in signed networks. Our experimental results indicate that KASGCN exhibits competitive or comparable performance to standard SGCNs across the tasks evaluated, with performance variability depending on the specific characteristics of the signed graph and the choice of parameter settings. These findings suggest that KASGCNs hold promise for enhancing signed graph analysis with context-dependent effectiveness.

cs.LG↗

arXiv2025

ABCD: Algorithm for Balanced Component Discovery in Signed Networks

The largest balanced element in signed graphs plays a vital role in helping researchers understand the fundamental structure of the graph, as it reveals valuable information about the complex relationships between vertices in the network. The challenge is an NP-hard problem; there is no current baseline to evaluate state-of-the-art signed graphs derived from real networks. In this paper, we propose a scalable state-of-the-art approach for the maximum balanced sub-graph detection in the network of any size. The proposed approach finds the largest balanced sub-graph by considering only the top $K$ balanced states with the lowest frustration index. We show that the ABCD method selects a subset from an extensive signed network with millions of vertices and edges, and the size of the discovered subset is double that of the state-of-the-art in a similar time frame.

cs.SI↗

arXiv2025

Scaling Frustration Index and Corresponding Balanced State Discovery for Real Signed Graphs

Structural balance modeling for signed graph networks presents how to model the sources of conflicts. The state-of-the-art focuses on computing the frustration index of a signed graph, a critical step toward solving problems in social and sensor networks and scientific modeling. The proposed approaches do not scale to large signed networks of tens of millions of vertices and edges. This paper proposes two efficient algorithms, a tree-based \emph{graphBpp} and a gradient descent-based \emph{graphL}. We show that both algorithms outperform state-of-art in terms of efficiency and effectiveness for discovering the balanced state for \emph{any} network size. We introduce the first comparison for large graphs for the exact, tree-based, and gradient descent-based methods. The speedup of the methods is around \emph{300+ times faster} than the state-of-the-art for large signed graphs. We find that the exact method excels at optimally finding the frustration for small graphs only. \emph{graphBpp} scales this approximation to large signed graphs at the cost of accuracy. \emph{graphL} produces a state with a lower frustration at the cost of selecting a proper variable initialization and hyperparameter tuning.

cs.SI↗

arXiv2025

GraphC: Parameter-free Hierarchical Clustering of Signed Graph Networks v2

Spectral clustering methodologies, when extended to accommodate signed graphs, have encountered notable limitations in effectively encapsulating inherent grouping relationships. Recent findings underscore a substantial deterioration in the efficacy of spectral clustering methods when applied to expansive signed networks. We introduce a scalable hierarchical Graph Clustering algorithm denominated GraphC. This algorithm excels at discerning optimal clusters within signed networks of varying magnitudes. GraphC aims to preserve the positive edge fractions within communities during partitioning while concurrently maximizing the negative edge fractions between communities. Importantly, GraphC does not require a predetermined cluster count (denoted as k). Empirical substantiation of GraphC 's efficacy is provided through a comprehensive evaluation involving fourteen datasets juxtaposed against ten baseline signed graph clustering algorithms. The algorithm's scalability is demonstrated through its application to extensive signed graphs drawn from Amazon-sourced datasets, each comprising tens of millions of vertices and edges. A noteworthy accomplishment is evidenced, with an average cumulative enhancement of 18.64% (consisting of the summation of positive edge fractions within communities and negative edge fractions between communities) over the second-best baseline for each respective signed graph. It is imperative to note that this evaluation excludes instances wherein all baseline algorithms failed to execute comprehensively.

cs.SI↗