Abnormal user detection has been a critical and widely studied research problem in social networks since these users can create significant risks to platform security and privacy leakage. Currently, graph-based models are commonly used for exploring the structured social network data and temporally dynamic user interactions, leading to significant advances in dynamic heterogeneous graph-based abnormal user detection. However, most existing approaches are correlation-driven and lack the ability to separate stable patterns from transient noise. Furthermore, these methods are highly dependent on inherent labels and fail to detect common few-shot anomalies in social networks. To address these issues, we propose CIFAD, a Causal-Invariant Few-shot Anomaly Detection method that improves few-shot anomaly detection with an active annotation strategy. Specifically, CIFAD first integrates a sparse lagged attention encoder to model multi-relational temporal interactions. Furthermore, it introduces causal-invariant subspace decomposition to disentangle stable causal signals from dynamic environmental noise and improve generalization. Finally, it designs an active annotation strategy based on influence functions and coverage optimization to maximize the utility of limited labels in a closed-loop process. Extensive experiments on multiple real-world social network datasets demonstrate that our method achieves higher accuracy than state-of-the-art methods, validating its robustness in abnormal user detection for social networks.
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Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-based approaches have demonstrated promising results in temporal graph learning, their performance remains limited when capturing complex multi-scale temporal dynamics. In this paper, we propose TFWaveFormer, a novel Transformer architecture that integrates temporal-frequency analysis with multi-resolution wavelet decomposition to enhance dynamic link prediction. Our framework comprises three key components: (i) a temporal-frequency coordination mechanism that jointly models temporal and spectral representations, (ii) a learnable multi-resolution wavelet decomposition module that adaptively extracts multi-scale temporal patterns through parallel convolutions, replacing traditional iterative wavelet transforms, and (iii) a hybrid Transformer module that effectively fuses local wavelet features with global temporal dependencies. Extensive experiments on benchmark datasets demonstrate that TFWaveFormer achieves state-of-the-art performance, outperforming existing Transformer-based and hybrid models by significant margins across multiple metrics. The superior performance of TFWaveFormer validates the effectiveness of combining temporal-frequency analysis with wavelet decomposition in capturing complex temporal dynamics for dynamic link prediction tasks. The code is available at https://github.com/SEUFHTong/TFWaveFormer.
Source localization has attracted increasing attention in recent years due to its vital role in governing the harmful propagation. However, existing localization methods do not fully consider the temporal characteristics in propagation and struggle to leverage the continuous-time information of real-world propagation scenarios. Moreover, the aggregation ability of GNN based localization models is limited by the structural noise commonly present in complicated real-world topologies. To address these challenges, a Spectral Neural Ordinary Differential Equation (SNODE) is proposed to infer the source in real-world continuous-time scenarios. First, the forward propagation is formulated as a flow based ODE system, and the source localization problem is transformed into an inverse ODE modeling task. Second, a neural process based on a graph variational autoencoder is introduced to encode global latent propagation patterns as a conditioning variable for the ODE system. Third, a spectral graph optimization is performed to suppress topological noise by filtering out high-frequency components that degrade the quality of graph aggregation in the neural process. Comprehensive experiments demonstrate that SNODE not only outperforms the optimal baseline in real-world continuous-time propagation scenarios with an average performance improvement of 43.8%, but also achieves consistently superior performance in synthetic discrete-time datasets with an improvement of 4.5%, highlighting its strong generalization ability in different propagation settings. Our code is available at https://github.com/cgao-comp/SNODE.
Illicit activities such as financial fraud and fake promotions are increasingly prevalent on social applications. The diverse behavioral structures of different types of illicit accounts pose difficulties in designing unified detection strategies. Unlike existing studies that focus on binary detection and often rely on user static profiles, we introduce a novel task: illicit account detection based solely on user behavior sequences. This task presents two key challenges: 1) illicit accounts often mimic benign users by performing normal-looking behavior subsequences, and 2) behaviors with the same action (e.g., add-friend, initiate-transaction) can serve different purposes, such as illicit or benign. To address these challenges, we propose a Pattern-aware Illicit Accounts Detection (PIAD) framework that consists of three components: 1) a dual-perspective pattern mining module that extracts category-specific self- and interaction-behavior patterns from behavior sequences to capture distinct behavioral regularities across different user types; 2) a contextualized action semantic encoding algorithm that aligns action codings with contextual dependencies among behaviors within user sequences to capture variations in purposes when behaviors with the same actions occur under different contexts; and 3) a pattern-aware fusion model that integrates the mined patterns with the context and interaction in behavior sequences to learn discriminative representations for detection. Extensive experiments on real-world datasets demonstrate that PIAD consistently outperforms state-of-the-art baselines with an average 7.51% improvement on F1 score.
Multimodal desire understanding, a task closely related to both emotion and sentiment that aims to infer human intentions from visual and textual cues, is an emerging yet underexplored task in affective computing with applications in social media analysis. Existing methods for related tasks predominantly focus on mining verbal cues, often overlooking the effective utilization of non-verbal cues embedded in images. To bridge this gap, we propose a Symmetrical Bidirectional Multimodal Learning Framework for Desire, Emotion, and Sentiment Recognition (SyDES). The core of SyDES is to achieve bidirectional fine-grained modal alignment between text and image modalities. Specifically, we introduce a mixed-scaled image strategy that combines global context from low-resolution images with fine-grained local features via masked image modeling (MIM) on high-resolution sub-images, effectively capturing intention-related visual representations. Then, we devise symmetrical cross-modal decoders, including a text-guided image decoder and an image-guided text decoder, which enable mutual reconstruction and refinement between modalities, facilitating deep cross-modal interaction. Furthermore, a set of dedicated loss functions is designed to harmonize potential conflicts between the MIM and modal alignment objectives during optimization. Extensive evaluations on the MSED benchmark demonstrate the superiority of our approach, which establishes a new state-of-the-art performance with 1.1% F1-score improvement in desire understanding. Consistent gains in emotion and sentiment recognition further validate its generalization ability and the necessity of utilizing non-verbal cues. Our code is available at: https://github.com/especiallyW/SyDES.
Propagation models are essential for modeling and simulating dynamic processes such as epidemics and information diffusion. However, existing tools struggle to scale to large-scale graphs that emerge across social networks, epidemic networks and so on, due to limited algorithmic efficiency, weak scalability, and high communication overhead. We present FS_GPlib, a unified library that enables efficient, high-fidelity propagation modeling on Web-scale graphs. FS_GPlib introduces a dual-acceleration framework: it combines micro-level synchronous message-passing updates with macro-level batched Monte Carlo simulation, leveraging high-dimensional tensor operations for parallel execution. To further enhance scalability, it supports distributed simulation via a novel target-node-based graph partitioning strategy that minimizes communication overhead while maintaining load balance. Theoretically, we show that under ideal assumptions, the runtime of simulations converges approximately to a constant. Extensive experiments demonstrate up to 35,000× speedup over standard libraries such as NDlib and execution of a full Monte Carlo simulation on a Web-scale (billion-edge) graph in 11 seconds while maintaining high simulation fidelity. FS_GPlib supports 29 propagation models—including epidemic and opinion dynamics and dynamic network models—and offers a lightweight Python API compatible with mainstream data science ecosystems. By addressing the unique challenges of modeling diffusion and cascades on the Web, FS_GPlib provides a scalable, extensible, and theoretically grounded solution for large-scale propagation analysis in epidemiology, social media analysis, and online network dynamics. Code available at: https://github.com/Allen-Ciel/FS_GPlib.
Opinion propagation research primarily focuses on phenomenon prediction rather than mechanism understanding, lacking interpretable frameworks to reveal underlying propagation dynamics. This limitation stems from two sources: existing methods employ end-to-end paradigms where parameters lack physical meanings, while available datasets suffer from incomplete hierarchical structures, coarse sentiment annotations, and limited domain coverage. To address these limitations, we introduce VISTA, a multi-dimensional opinion propagation dataset providing complete hierarchical structures, fine-grained emotional annotations, and cross-domain coverage. Based on this dataset, we propose an interpretable modeling framework integrating high-dimensional Hawkes processes with graph neural networks, enabling parametric expression of propagation mechanisms through event space constructed from emotional and reply level combinations. Through interpretable parameter analysis, we reveal three mechanistic patterns: differential emotional propagation strength, asymmetric hierarchical excitation, and temporal memory effects. Our framework establishes quantitative foundations for understanding opinion propagation dynamics, achieving best performance in sentiment prediction and structural consistency tasks while providing the first benchmark for multi-dimensional propagation mechanism analysis.
Rare social events are infrequent yet influential incidents. Predicting such events is practically significant yet inherently challenging due to their extreme scarcity in web-based event stream. Existing studies view this task as an imbalanced classification problem and adopt static rebalancing methods to mitigate scarcity. However, they (1) ignore inter-event dependency that represents the interactions between different event streams, failing to capture precursors that lead to rare events and fundamentally limits performance. They (2) overlook intra-event dependency between different time points within single event stream, which prevents the model from adapting to shifting event patterns and degrades its generalization ability. To this end, we propose a novel Mediator Learning (ML) framework, which introduces mediators to explicitly model complex dependencies within web-based event streams. Specifically, we propose (1) Precursor Event Router (PER) that utilizes an information-theoretic routing approach to extract precursor events as mediators from massive event streams. Based on extracted mediators, (2) Conditional Hierarchical Graph Network (CHG) is introduced to model observed events, mediators and rare events into bottom-up graph levels, where its upper-level propagation is conditioned on bottom-level probability distribution. Jointly, these two modules decompose the imbalanced task into two more balanced stages, which not only mitigates the scarcity of rare events but also explicitly model inter-event dependencies, so as to capture precursor events leading to target rare events. Finally, we design (3) Adaptive Information Regularizer (AIR) to optimize the two stages. It dynamically adjusts the information flow between two stages, which models intra-event dependencies and facilitate adaption to drifting event patterns. We theoretically reveal the effectiveness of ML by framing it within Information Bottleneck (IB) principle. Extensive experiments show our superior accuracy and interpretability compared with SOTA.
Social event prediction has garnered increasing attention in web-centered society. Most existing studies represent web-based event stream as chronological graph sequences, then leverage RNNs and GNNs to model temporal and relational patterns. However, this paradigm is inherently flawed: (1) RNNs struggle to capture long-term temporal dependencies, ignoring those temporally distant but influential events. (2) Spatio-temporal GNNs exhibit high computational complexity on large-scale real-time event streams, which hinders their web applications. To this end, we explore a novel paradigm called Fourier Graph Learning from the perspective of frequency domain. Specifically, we first define a novel data structure called Fourier Graph (FG). In FG, both nodes and edges are complex vectors, with real part encoding semantics and imaginary part representing semantic-specific temporal patterns. These temporal patterns are obtained by semantic-aware frequency filter, which utilizes semantics as guidance to adaptively incorporates both long-term dependency and short-term dynamic. Based on FG, we further propose Fourier Graph Neural Network (FGNN). It replaces time-domain convolution with frequency-domain multiplication for efficient aggregation. FGNN also includes a complex-valued event decoder, which fully leverages semantics and temporal patterns from complex space to predict future event probabilities. Extensive experiments show our superior performance with higher accuracy, less complexity and better interpretability compared with baselines.
Inferring the underlying structures of diffusion networks based on observed diffusion results is a fundamental problem in network analysis. Traditional approaches typically address this problem by inferring each diffusion network in isolation, relying on the assumption that sufficient observation data is available for each individual inference task. However, in many real-world scenarios, it is common to observe diffusion processes occur across multiple networks with similar structures, while the amount of observable data collected on each network is often limited. In this work, we study how to infer multiple similar diffusion networks jointly with limited observation data for each network. To this end, we propose a novel iterative strategy which in turn updates the inference results for all diffusion networks by exploiting the similarity between the networks, and theoretically guarantee the monotonicity and convergence of the iterative process. Extensive experiments on both synthetic and real-world networks demonstrate that our method not only achieves superior inference accuracy compared to existing techniques, but also maintains high computational efficiency.
Graph Neural Networks (GNNs) demonstrate promising performance in data mining yet exhibit inherent vulnerabilities to adversarial attacks. Even imperceptible perturbations degrade model performance, seriously hindering the application of GNNs in reality. In recent years, adversarial defense methods based on model architecture have gained attention for their effectiveness. However, they exhibit limited effectiveness against emerging black-box influence maximization attacks (IMAs), which aim to maximize the spread of feature perturbations through a group of influential nodes. This may leave a potential risk in real-world applications. To address this issue, we propose a Graph Adversarial Defense method based on the Hilbert-Schmidt Independence Criterion (HSIC-GAD). Specifically, the proposed method leverages hidden representations to capture the dependence between preprocessed node features and label information. On this basis, we design a regularizer that simultaneously preserves the most relevant information for downstream tasks while filtering out adversarial perturbations from the input data. A simple theoretical analysis shows that the HSIC regularizer can reduce the sensitivity of the model to adversarial inputs. Additionally, it exhibits strong universality, consistently enhancing the adversarial robustness of diverse models. Extensive experiments on real-world datasets demonstrate that HSIC-GAD outperforms state-of-the-art defense methods against IMAs.
In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is leveraged for recommendations, consequently diminishing explainability. However, existing explainers can only identify the topological information in social networks that significantly influences recommendations, failing to further explain the synergistic effects among this information. Inspired by existing findings that synergistic effects enhance mutual information between inputs and predictions to generate information gain, we extend this discovery to graph data. We quantify graph information gain to identify subgraphs embodying synergistic effects. Based on the theoretical insights, we propose SemExplainer, which explains synergistic effects by identifying subgraphs that embody them. SemExplainer first extracts explanatory subgraphs from multi-view social networks to generate preliminary importance explanations for recommendations. A conditional entropy optimization strategy to maximize information gain is developed, thereby further identifying subgraphs that embody synergistic effects from explanatory subgraphs. Finally, SemExplainer searches for paths from users to recommended items within the synergistic subgraphs to generate explanations for the recommendations. Extensive experiments on three datasets demonstrate the superiority of SemExplainer over baseline methods, providing superior explanations of synergistic effects. The implementation is available at https://github.com/yushuowiki/SemExplainer.
Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines.
With the growing scale of social media, social event detection and evolution modeling have attracted increasing attention. Graph neural networks (GNNs) and transformer-based pre-trained language models (PLMs) have become mainstream approaches in this area. However, existing methods still face three major challenges. First, the sheer volume of social media messages makes learning resource-intensive. Second, the fragmentation of social media messages often impedes the model's ability to capture a comprehensive view of the events. Third, the lack of structured temporal context has hindered the development of effective models for event evolution, limiting users' access to event information. To address these challenges, we propose a foundation model for unsupervised Social Event Detection and Evolution, namely RagSEDE. Specifically, RagSEDE introduces a representativeness- and diversity-driven sampling strategy to extract key messages from massive social streams, significantly reducing noise and computational overhead. It further establishes a novel paradigm based on Retrieval Augmented Generation (RAG) that enhances PLMs in detecting events while simultaneously constructing and maintaining an evolving event knowledge base. Finally, RagSEDE leverages structural information theory to dynamically model event evolution keywords for the first time. Extensive experiments on two public datasets demonstrate the superiority of RagSEDE in open-world social event detection and evolution.
Social media's openness fosters opinion exchange but complicates guiding users toward global consensus. Existing approaches are often invasive, modifying opinions or forcing cross-group interactions, undermining autonomy and triggering resistance. They also lack long-term planning, risking macro-level polarization despite local agreement. To address this, we propose H-NeiFi, a hierarchical, non-invasive opinion guidance framework. It models experts and non-experts separately and introduces a neighbor filtering mechanism that adaptively shapes communication without altering user opinions. Using multi-agent reinforcement learning with a long-term reward, H-NeiFi optimizes information flow while preserving interaction autonomy. Experiments show that it accelerates consensus by 22.0% to 30.7% and achieves global convergence even without experts, providing a natural and efficient paradigm for social network governance. Our code is available at: https://github.com/shijunguo44/H-NeiFi.
Content moderation is a major challenge for online platforms. While user-driven blocking is a common tool, its dynamics are usually hidden as moderation data is private. Bluesky makes moderation actions public-by-design, providing an unprecedented opportunity to study a community-driven moderation ecosystem at scale. We leverage this transparency to (1) map the ecosystem of moderation blocking actions across 34 million users, including both individual blocks and the through blocklists, (2) identify the signals that correlate with blocking, and (3) measure the consequences of these actions. We demonstrate that community blocking is widespread, with a volume several orders of magnitude higher than official takedowns, and affects the visibility of more than 90 % of Bluesky content. The blocked accounts represent the most active, popular, toxic, and politically inclined users. However, different blocklists target different types of accounts and behaviors. Finally, blocking does not decrease the popularity and activity of the blocked users and has a limited effect on the social graph. By quantifying its dynamics and trade-offs, our study provides empirical grounding for designing future moderation systems that are transparent, pluralistic, and resistant to centralized control. Taken together, this study provides the first large-scale, quantitative analysis of a community-driven moderation ecosystem, demonstrating how individual and collective interventions influence user behavior.
Graph neural networks (GNNs) have become the cornerstone of graph representation learning in real-world applications, especially in social networks such as Facebook and Twitter, where user interactions naturally form graph-structured data. To safeguard sensitive information, including user attributes and personal connections, locally private graph learning protocols employ local differential privacy (LDP) to provide rigorous user-level guarantees. Despite these protections, we show that such protocols remain highly vulnerable to data poisoning attacks. Our approach targets the full-privacy setting, where both node features and edges are LDP-protected, and executes coordinated manipulations that significantly degrade utility, such as node classification accuracy, across multiple social network benchmarks. To counter these threats, we propose CureNet, a defense framework with four key components: (1) local data perturbation for privacy, (2) trimmed screening to filter abnormal submissions, (3) privacy-aware fake node pruning to remove sophisticated adversaries, and (4) a utility enhancement module to recover graph learning performance under privacy constraints. Extensive experiments on four real-world social network datasets—LastFM, Twitch, Github, and Facebook—demonstrate both the severe impact of our attack and the effectiveness of our defense. This study underscores the critical need to secure privacy-preserving graph learning against sophisticated poisoning threats.
Spamming activities (e.g., fake reviews, click farming, and deceptive content promotion) are increasingly conducted through collusive groups that exploit collective dynamics to manipulate platform metrics and mislead users, posing serious threats to the fairness, credibility, and functionality of online systems. To counteract these harmful behaviors, the task of spam detection has emerged as a critical area of research. However, existing detection methods generally remain limited in three key aspects: (i) They treat detection as a standard classification task, where representation learning and optimization are loosely coupled and suboptimal for capturing complex behaviors; (ii) They rely primarily on individual-level representation modeling, making it difficult to detect collective cheating strategies; (iii) They lack dedicated objective functions explicitly designed to characterize group-level spamming activities. To overcome these limitations, we introduce a collusion-aware Set-level learning framework (SetDet) that redefines the spam Detection task as a unified setwise optimization problem. Our approach offers three core advantages: (i) It enables end-to-end optimization by jointly learning representations and performing detection in a single, integrated process; (ii) It incorporates a model-level design for collusion representation, effectively capturing the temporal and relational patterns of coordinated spam; (iii) It pioneers a dedicated set-level optimization criterion that aligns closely with the structural characteristics of group-based cheating behaviors and accounts for class imbalance in real-world scenarios. Extensive experiments confirm the generalizability and superior performance of our framework across diverse spam scenarios and collusion strategies.
Information diffusion prediction aims to forecast the temporal spread of opinions and behaviors by identifying potential adopters. Existing methods typically treat information diffusion as a sequence of individual adoptions and rely on computationally expensive pairwise (one-to-one) influence computations, often restricting predictions to just the next adopter. This individual-level paradigm both misrepresents real-world collective (many-to-many) influences and suffers a critical efficiency trade-off: to remain feasible, such models must truncate long diffusion histories, thereby overlooking early initiators and opinion leaders. To overcome these limitations, we formalize a more practical task: Group-based Information Diffusion Prediction, and propose an effective and scalable GRID framework. Specifically, GRID first learns group-oriented graph embeddings via a task-regularized information bottleneck objective, which amplifies key influence pathways and produces reliable user embeddings for group identification. Built on these embeddings, the core GroupAttn module captures inter-group influence while reducing complexity from quadratic to linear in cascade length. This enables the modeling of ultra-long cascades (exceeding 10,000 users) without truncation while preserving representational fidelity within a provable error bound. Finally, a group-wise objective guides the model to predict semantically meaningful future groups. Extensive experiments on four real-world datasets show that GRID outperforms ten state-of-the-art baselines by an average of 10.65% in accuracy, while achieving an order-of-magnitude gain in efficiency and extending the supported cascade length by up to 10 times.
Retrieval-augmented generation (RAG) has improved large language models (LLMs) on knowledge-intensive tasks, yet most systems assume static facts and struggle when answers depend on serialized and dynamic data, like time--e.g., ordering events, aligning facts to valid intervals, or planning actions under evolving conditions. This paper presents Knowledge-Graph Iterative Retrieval-Augmented Generation (KG-iRAG), a framework specialized for temporal reasoning. KG-iRAG couples a time-aware planner with a knowledge graph (KG) to iteratively fetch and compose evidence along a temporal axis. Concretely, it (i) represents events and facts with explicit timestamps and validity intervals; (ii) propagates temporal constraints through iterative retrieval using operators; and (iii) verifies temporal consistency while refining intermediate hypotheses, enabling step-by-step deduction for queries that mix knowledge retrieval with inference. Across public temporal QA benchmarks, KG-iRAG consistently improves accuracy and calibration over strong RAG baselines while reducing unnecessary retrieval through targeted, constraint-guided steps. To stress-test real-time decision queries, three application-oriented datasets (weatherQA-Irish, ~ weatherQA-Sydney, and~ trafficQA-TFNSW) are additionally constructed and tested alongside existing temporal benchmarks. The results demonstrate that injecting temporal structure into KG-driven RAG yields robust gains on multi-step, time-dependent queries, advancing the state of temporal reasoning with LLMs.