Recent advances in large language models (LLMs), such as GPT and Llama, have driven significant progress in natural language processing and diverse AI applications. In this paper, we explore how LLMs can enhance the construction of heterogeneous citation networks by integrating rich contextual information derived from LLMs. We propose a metadata-driven augmentation that generates concise factual descriptions for sparse fields in citation metadata, including keywords, venues, and author affiliations. These contexts are encoded with DeBERTa and integrated as node features in a knowledge-enriched heterogeneous network. Additionally, to mitigate LLM hallucinations, we employed Chain-of-Thought (CoT)-based prompting and evaluated the quality of the generated context. Experimental results demonstrate that our LLM-powered context augmentation improves author classification by 2.0%-4.5% and author clustering by 8.9%-18.1%, outperforming traditional feature engineering methods. The dataset and source code are available at https://github.com/inthwan/Metadata-Meets-LLMs.

Community detection in signed networks is challenging due to the presence of both positive and negative edges, which violate the homophily assumption commonly used in traditional methods. In this paper, we present CR-SGCN, an unsupervised framework for community detection in signed networks. It combines a signed GCN encoder, a soft community assignment layer, and a degree-corrected stochastic block model decoder. To enhance boundary separation, we introduce an edge-level signed conductance regularization that pulls intra-community embeddings closer and pushes inter-community ones apart. Without requiring labels, CR-SGCN effectively captures community structure even under edge sparsity. Experiments on real-world signed networks show consistent gains in signed modularity and structural separation over existing baselines. The results demonstrate the robustness and effectiveness of CR-SGCN for unsupervised signed community detection.

Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored. Existing approaches mainly target classification and representation-level debiasing, which cannot fully address the continuous nature of node-level regression. We propose FnRGNN, a fairness-aware in-processing framework for GNN-based node regression that applies interventions at three levels: (i) structure-level edge reweighting, (ii) representation-level alignment via MMD, and (iii) prediction-level normalization through Sinkhorn-based distribution matching. This multi-level strategy ensures robust fairness under complex graph topologies. Experiments on four real-world datasets demonstrate that FnRGNN reduces group disparities without sacrificing performance. Code is available at https://github.com/sybeam27/FnRGNN.

To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, which aligns with the structural properties of heterogeneous graphs. However, despite heterogeneous graphs inherently possessing diverse power-law structures, most hyperbolic heterogeneous graph embedding models use a single hyperbolic space for the entire heterogeneous graph, which may not effectively capture the diverse power-law structures within the heterogeneous graph. To address this limitation, we propose Multi-hyperbolic Space-based heterogeneous Graph Attention Network (MSGAT), which uses multiple hyperbolic spaces to effectively capture diverse power-law structures within heterogeneous graphs. We conduct comprehensive experiments to evaluate the effectiveness of MSGAT. The experimental results demonstrate that MSGAT outperforms state-of-the-art baselines in various graph machine learning tasks, effectively capturing the complex structures of heterogeneous graphs.

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance.

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data.

Identifying cohesive subgraphs in hypergraphs is a fundamental problem that has received recent attention in data mining and engineering fields. Existing approaches mainly focus on a strongly induced subhypergraph or edge cardinality, overlooking the importance of the frequency of co-occurrence. In this paper, we propose a new cohesive subgraph named (k,g)-core, which considers both neighbour and co-occurrence simultaneously. The (k,g)-core has various applications including recommendation system, network analysis, and fraud detection. To the best of our knowledge, this is the first work to combine these factors. We extend an existing efficient algorithm to find solutions for (k,g)-core. Finally, we conduct extensive experimental studies that demonstrate the efficiency and effectiveness of our proposed algorithm.

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss?

This paper proposes LinkBlackHole*, a novel algorithm for finding communities that are (i) overlapping in nodes and (ii) mixing (not separating clearly) in links. There has been a small body of work in each category, but this paper is the first one that addresses both. For this purpose, LinkBlackHole* incorporates the advantages of both the link-space transformation and the black hole transformation. Thorough experiments show superior quality of the communities detected by LinkBlackHole* to those detected by other state-of-the-art algorithms.

This paper proposes LinkBlackHole*, a novel algorithm for finding communities that are (i) overlapping in nodes and (ii) mixing (not separating clearly) in links. There has been a small body of work in each category, but this paper is the first one that addresses both. LinkBlackHole* is a merger of our earlier two algorithms, LinkSCAN* and BlackHole, inheriting their advantages in support of highly-mixed overlapping communities. The former is used to handle overlapping nodes, and the latter to handle mixing links in finding communities. Like LinkSCAN and its more efficient variant LinkSCAN*, this paper presents LinkBlackHole and its more efficient variant LinkBlackHole*, which reduces the number of links through random sampling. Thorough experiments show superior quality of the communities detected by LinkBlackHole* and LinkBlackHole to those detected by other state-of-the-art algorithms. In addition, LinkBlackHole* shows high resilience to the link sampling effect, and its running time scales up almost linearly with the number of links in a network.

Amulti-layer graphconsists of multiple layers of weighted graphs, where the multiple layers represent the different aspects of relationships. Considering multiple aspects (i.e., layers) together is essential to achieve a comprehensive and consolidated view. In this article, we propose a novel framework ofdifferential flattening, which facilitates the analysis of multi-layer graphs, and apply this framework to community detection. Differential flattening merges multiple graphs into a single graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with multiple layers is doneindependentlyof a specific community detection algorithm, whereas previous approaches rely on a specific algorithm. Thus, any algorithm for a single graph becomes applicable to multi-layer graphs. Second, the contribution of each layer to the single graph is determinedautomaticallyfor the maximum clustering coefficient. Since differential flattening is formulated by an optimization problem, the optimal solution is easily obtained by well-known algorithms such as interior point methods. Extensive experiments were conducted using the Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks as well as the DBLP, 20 Newsgroups, and MIT Reality Mining networks. The results show that our approach of differential flattening leads to discovery of higher-quality communities than baseline approaches and the state-of-the-art algorithms.

With regard to social network analysis, we concentrate on two widely-accepted building blocks: community detection and graph drawing. Although community detection and graph drawing have been studied separately, they have a great commonality, which means that it is possible to advance one field using the techniques of the other. In this paper, we propose a novel community detection algorithm for undirected graphs, called BlackHole, by importing a geometric embedding technique from graph drawing. Our proposed algorithm transforms the vertices of a graph to a set of points on a low-dimensional space whose coordinates are determined by a variant of graph drawing algorithms, following the overall procedure of spectral clustering. The set of points are then clustered using a conventional clustering algorithm to form communities. Our primary contribution is to prove that a common idea in graph drawing, which is characterized by consideration of repulsive forces in addition to attractive forces, improves the clusterability of an embedding. As a result, our algorithm has the advantages of being robust especially when the community structure is not easily detectable. Through extensive experiments, we have shown that BlackHole achieves the accuracy higher than or comparable to the state-of-the-art algorithms.

In this paper, for overlapping community detection, we propose a novel framework of the link-space transformation that transforms a given original graph into a link-space graph. Its unique idea is to consider topological structure and link similarity separately using two distinct types of graphs: the line graph and the original graph. For topological structure, each link of the original graph is mapped to a node of the link-space graph, which enables us to discover overlapping communities using non-overlapping community detection algorithms as in the line graph. For link similarity, it is calculated on the original graph and carried over into the link-space graph, which enables us to keep the original structure on the transformed graph. Thus, our transformation, by combining these two advantages, facilitates overlapping community detection as well as improves the resulting quality. Based on this framework, we develop the algorithm LinkSCAN that performs structural clustering on the link-space graph. Moreover, we propose the algorithm LinkSCAN* that enhances the efficiency of LinkSCAN by sampling. Extensive experiments were conducted using the LFR benchmark networks as well as some real-world networks. The results show that our algorithms achieve higher accuracy, quality, and coverage than the state-of-the-art algorithms.