Research on Graph Structure Learning (GSL) provides key insights for graph-based clustering, yet current methods like Graph Neural Networks (GNNs), Graph Attention Networks (GATs), and contrastive learning often rely heavily on the original graph structure. Their performance deteriorates when the original graph's adjacency matrix is too sparse or contains noisy edges unrelated to clustering. Moreover, these methods depend on learning node embeddings and using traditional techniques like k-means to form clusters, which may not fully capture the underlying graph structure between nodes. To address these limitations, this paper introduces DeSE, a novel unsupervised graph clustering framework incorporating Deep Structural Entropy. It enhances the original graph with quantified structural information and deep neural networks to form clusters. Specifically, we first propose a method for calculating structural entropy with soft assignment, which quantifies structure in a differentiable form. Next, we design a Structural Learning layer (SLL) to generate an attributed graph from the original feature data, serving as a target to enhance and optimize the original structural graph, thereby mitigating the issue of sparse connections between graph nodes. Finally, our clustering assignment method (ASS), based on GNNs, learns node embeddings and a soft assignment matrix to cluster on the enhanced graph. The ASS layer can be stacked to meet downstream task requirements, minimizing structural entropy for stable clustering and maximizing node consistency with edge-based cross-entropy loss. Extensive comparative experiments are conducted on four benchmark datasets against eight representative unsupervised graph clustering baselines, demonstrating the superiority of the DeSE in both effectiveness and interpretability.

Tables are ubiquitous across various domains for concisely representing structured information. Empowering large language models (LLMs) to reason over tabular data represents an actively explored direction. However, since typical LLMs only support one-dimensional (1D) inputs, existing methods often flatten the two-dimensional (2D) table structure into a sequence of tokens, which can severely disrupt the spatial relationships and result in an inevitable loss of vital contextual information. In this paper, we first empirically demonstrate the detrimental impact of such flattening operations on the performance of LLMs in capturing the spatial information of tables through two elaborate proxy tasks. Subsequently, we introduce a simple yet effective positional encoding method, termed "2D-TPE" (Two-Dimensional Table Positional Encoding), to address this challenge. 2D-TPE enables each attention head to dynamically select a permutation order of tokens within the context for attending to them, where each permutation represents a distinct traversal mode for the table, such as column-wise or row-wise traversal. 2D-TPE effectively mitigates the risk of losing essential spatial information while preserving computational efficiency, thus better preserving the table structure. Extensive experiments across five benchmarks demonstrate that 2D-TPE outperforms strong baselines, underscoring the importance of preserving the table structure for accurate table comprehension. Comprehensive analysis further reveals the substantially better scalability of 2D-TPE to large tables than baselines.

Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes.However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency and unreliability of existing methods.Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability.To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional (2D) structural entropy within a potential game framework.In the game, nodes decide to stay in the current community or move to another based on a strategy that maximizes the 2D structural entropy utility function.Additionally, we introduce a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.Experimental results on real-world networks demonstrate that CoDeSEG is the fastest method available and achieves state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1 scores.

With long-tailed data and complex label hierarchy, hierarchical text classification (HTC) is a challenging multi-label text classification task. Applying prompts to pre-trained language models (PLMs) has recently become a mainstream approach in HTC. However, existing prompt-based models experience a significant drop in classification performance on tail labels. Due to the imbalanced data, HTC models still face two challenges. First, text embeddings, learned for classification, often lack distinctiveness for tail categories. Second, label embeddings suffer from significant degeneration, especially for tail labels. To address these issues, in this paper, we propose a novel Hierarchical Text Classification Optimization method via Structural Entropy and SIngular Spectrum Smoothing, namely SIHTC. SIHTC contains two parts: text embedding optimization and label embedding optimization. First, based on the structural information theory, we design a tree aggregation network and construct encoding trees to minimize the structural entropy of texts under the hierarchical labels. In this manner, SIHTC injects label structural information into text embeddings, hierarchically optimizing the embedding space by enclosing the text embeddings within related ground truth labels while separating them from unrelated ground truth labels. Second, we propose a global and local singular spectrum smoothing regularization method to maximize the area under the singular value curve. In this way, SIHTC decreases representation degeneration and learns label embeddings with improved label generalization capability. Extensive experiments are conducted on three popular HTC datasets. The results show that SIHTC outperforms all baseline methods, especially with an advantage in handling tail labels, indicating the effectiveness of the above two optimizations

News recommendation aims to offer potentially interesting news items to a specific user, guided by his historical browsing behaviors. Existing methods failed to effectively address the knowledge sparsity issue that the user may have sparse behaviors and the news may own sparse features. To address the problem, we propose a graph-based news recommendation model with intrinsic interest enhancement, named GIIE , leveraging intrinsic interests and neighbor information to enhance the representation of sparse users and news. Concretely, to fully take advantage of the intrinsic interests, we design an interest encoder based on an interest-type graph with a learnable structure and explore the interest embeddings from news types. Then, we inject the obtained interest embeddings into news and represent the user by aggregating the clicked news under the same interest and across different interests sequentially. These interests can build a bridge between users so users with sparse behaviors can implicitly share knowledge with other users, thereby enhancing their representation. To properly introduce the neighbor knowledge, we propose a graph-based neighbor enhancing mechanism. First, we design a news relation graph and a user relation graph in encoders. Then, based on these graphs, we take the attention module to aggregate additional knowledge from neighbors, enhancing sparse news and user representations. To avoid feature ambiguity, we adopt a way to represent the current item (user and news) and its neighbors separately and then do adaptive aggregation. We evaluate GIIE on the public news recommendation datasets MIND-Large and MIND-Small. Experimental results show that our model can solve the knowledge-sparse problem and outperforms current state-of-the-art models in four indicators.

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware gridlike graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://github.com/KunPunCN/PT-GCN.

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the common lesser-spoken languages relatively unexplored. Thus, in this work, we present a GNN-based framework that integrates cross-lingual word embeddings into the process of graph knowledge distillation for detecting events in low-resource language data streams. To achieve this, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Specifically, to extract sufficient useful knowledge, we propose a hybrid distillation method that consists of both feature-wise and relation-wise information. To transfer both kinds of knowledge in an effective way, we add a cross-lingual module in the feature-wise distillation to eliminate the language gap and selectively choose beneficial relations in the relation-wise distillation to avoid distraction caused by teachers’ misjudgments. Our proposed CLKD framework also adopts different configurations to suit both offline and online situations. Experiments on real-world datasets show that the framework is highly effective at detection in languages where training samples are scarce.

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, for example, Graph Neural Network, Transformer, and so on, which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective, and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: posting type distribution , posting influence , and follow-to-follower ratio . Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Third, a new community labeling method is presented to distinguish social bot communities by computing the user’s stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with 10 representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on 4 real social network datasets.

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://github.com/RiemanGraph/.

In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; then, with the information of the two maximum cliques, the relevance between experts and projects is calculated. After that, according to the descending order of the relevant degree, the candidates are ranked. Finally, the experts, who are the top N to projects, are recommended. The experiments on five domain datasets are made and the results show that the proposed method can improve the effect of review expert recommendation, and the F-value increases by an average of 5% than without considering the relevance among topics and the relationship among experts.

Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question retrieval and question diversification. We first propose probabilistic retrieval models to locate candidate questions that are relevant to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative for individual reviews.

This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple perspectives, the task of COM is to present the opinions of the individual perspectives on the topic, and furthermore to quantify their difference. This general problem subsumes many interesting applications, including opinion summarization and forecasting, government intelligence and cross-cultural studies. We propose a novel unsupervised topic model for contrastive opinion modeling. It simulates the generative process of how opinion words occur in the documents of different collections. The ad hoc opinion search process can be efficiently accomplished based on the learned parameters in the model. The difference of perspectives can be quantified in a principled way by the Jensen-Shannon divergence among the individual topic-opinion distributions. An extensive set of experiments have been conducted to evaluate the proposed model on two datasets in the political domain: 1) statement records of U.S. senators; 2) world news reports from three representative media in U.S., China and India, respectively. The experimental results with both qualitative and quantitative analysis have shown the effectiveness of the proposed model.