Community search is a personalized community discovery problem designed to identify densely connected subgraphs containing the query node. Recently, community search in heterogeneous information networks (HINs) has received considerable attention. Existing methods typically focus on modeling relationships in HINs through predefined meta-paths or user-specified relational constraints. However, metapath-based methods are primarily designed to identify single-type communities with nodes of the same type rather than multi-type communities involving nodes of different types. Constraint-based methods require users to have a good understanding of community patterns to define a suitable set of relational constraints, which increases the burden on users. In this paper, we propose FCS-HGNN, a novel method for flexibly identifying both single-type and multi-type communities in HINs. Specifically, FCS-HGNN extracts complementary information from different views and dynamically considers the contribution of each relation instead of treating them equally, thereby capturing more fine-grained heterogeneous information. Furthermore, to improve efficiency on large-scale graphs, we further propose LS-FCS-HGNN, which incorporates i) the neighbor sampling strategy to improve training efficiency, and ii) the depth-based heuristic search strategy to improve query efficiency. We conducted extensive experiments to demonstrate the superiority of our proposed methods over state-of-the-art methods, achieving average improvements of 14.3% and 11.1% on single-type and multi-type communities, respectively.

Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily improve overall generalization performance. Moreover, limited research has focused on the impact of mixed biases, which are more prevalent and demanding in real-world scenarios. To address these limitations, we propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs). Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations through the backdoor adjustment. Meanwhile, independence constraint is introduced to improve the discriminability and stability of causal and spurious features in complex biased environments. Essentially, CIE eliminates different types of data biases from a unified perspective, without the need to design separate methods for each bias as before. To evaluate the performance under specific types of data biases, mixed biases, and low-resource scenarios, we conducted comprehensive experiments on five publicly available datasets. Experimental results demonstrate that our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.

Privacy concerns on social networks have received extensive attention in recent years. The task of user identity linkage (UIL), which aims to identify corresponding users across different social networks, poses a threat to privacy if applied unethically. Sensitive user information would be inferred with cross-network identity linkages. A feasible solution to this issue is to design an adversarial strategy that degrades the matching performance of UIL models. Nevertheless, most of the current adversarial attacks on graphs are tailored towards models working within a single network, failing to account for the challenges presented by cross-network learning tasks such as UIL. Also, in real-world scenarios, the adversarial strategy against UIL has more constraints as service providers can only add perturbations to their own networks. To tackle these challenges, this paper proposes a novel poisoning strategy to prevent nodes in a target network from being linked to other networks by UIL algorithms. Specifically, the UIL problem is formalized in the kernelized topology consistency perspective, and the objective is formulated as maximizing the structural variations in the target network before and after modifications. To achieve this, a novel graph kernel is defined based on earth mover's distance (EMD) in the edge-embedding space. In terms of efficiency, a fast attack strategy is proposed using greedy searching and a lower bound approximation of EMD. Results on three real-world datasets demonstrate that the proposed method outperforms six baselines and reaches a balance between effectiveness and imperceptibility while being efficient.

Social network alignment is widely applied in web applications for identifying corresponding nodes across different networks, such as linking users across two social networks. Existing methods for social network alignment primarily rely on alignment consistency, assuming that nodes with similar attributes and neighbors are more likely to be aligned. However, distributional discrepancies in node attributes and neighbors across different networks would bring biases in alignment consistency, leading to inferior alignment performance. To address this issue, we conduct a causal analysis of alignment consistency. Based on this analysis, we propose a novel model called CANA that uses causal inference approaches to mitigate biases and enhance social network alignment. Firstly, we disentangle observed node attributes into endogenous features and exogenous features with multi-task learning. Only endogenous features are retained to overcome node attribute discrepancies. To eliminate biases caused by neighbors discrepancies, we propose causal-aware attention mechanisms and integrate them in graph neural network to reweight contributions of different neighbors in alignment consistency comparison. Additionally, backdoor adjustment is applied to reduce confounding effects and estimate unbiased alignment probability. Through experimental evaluation on four real-world datasets, the proposed method demonstrates superior performance in terms of alignment accuracy and top-k hits precision.

Graph domain adaptation models have become instrumental in addressing cross-network learning problems due to their ability to transfer abundant label and structural knowledge from source graphs to target graphs. A crucial step in transfer involves measuring domain discrepancy, which refers to distribution shifts between graphs from source and target domains. While conventional models simply provide a node-level measurement, exploiting information from different levels of network hierarchy is intuitive. As each hierarchical level characterizes distinct and meaningful properties or functionalities of the original graph, integrating domain discrepancy based on such hierarchies should contribute to a more precise domain discrepancy measurement. Moreover, class conditional distribution shift is often overlooked in node classification tasks, which could potentially lead to sub-optimal performance. To address the above limitations, we propose a new graph domain adaptation model and apply it to cross-network node classification tasks. Specifically, a hierarchical pooling model to extract meaningful and adaptive hierarchical structures is designed, where both marginal and class conditional distribution shifts on each hierarchical level are jointly minimized. The effectiveness is demonstrated through theoretical analysis and experimental studies across various datasets.

Graph domain adaptation models are widely adopted in cross-network learning tasks to transfer labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain adaptation models. On one side, they are primarily tested for the specific cross-network node classification task, leaving tasks at edge-level and graph-level largely under-explored. Moreover, they are primarily examined in limited scenarios, such as social networks or citation networks, needing more validation in richer scenarios. As comprehensively assessing models could enhance model practicality in real-world applications, we propose a benchmark known as OpenGDA. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models. The benchmark experiments highlight the challenges of applying GDA models to real-world applications with consistent good performance, and they potentially provide insights to future research. As an emerging project, OpenGDA will be regularly updated with new datasets and models. It could be accessed from https://github.com/Skyorca/OpenGDA.

User identity linkage aims to link users with the same identities across different social networks. Recently, re- searchers model the similarities of users’ behaviors such as Point of Interests(PoIs) or User Generated Contents(UGCs) to predict the identities of users. However, it is non-trivial to solve the problem due to the following challenges: 1) PoIs are always sparse in the non-location-based social platforms, and it is impractical to measure the similarities of users solely with PoIs; 2) The similarities of hierarchical are hierarchical from the view of word, phrase, and sentence. How to model the hierarchical structure remains a key challenge; 3) The unreliable semantics of words. Two different words may refer to the same physical appearance of users, indicating that users are with the same identities.To tackle the above problems, we propose UGCLink, a knowledge distillation framework that models UGCs to predict user identities. Two main components are included in the framework, where the student network aims to model the similarities of UGCs and the teacher network guides the student network to learn better word embeddings that reveal the physical appearance of users. Besides, the teacher network, a document classification model that classifies UGCs into the categories of PoIs, is trained to guide the word embedding learning process in the student network to circumvent the unreliable semantic problem. We demonstrate that our proposed method outperforms the state- of-the-art methods by more than 11% in terms of AUC score.

Nowadays, users are encouraged to activate across multiple online social networks simultaneously. User identity linkage, which aims to reveal the correspondence among different accounts across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, information from different social platforms may also be asymmetric, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel user identity linkage framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external text-location pairs. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.

Information gathered from multiple sources on the Web often exhibits conflicts. This phenomenon motivates the need of truth discovery, which aims to automatically find the true claim among multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates, optimization or probabilistic models. Although these methods have shown their own effectiveness, they have a common limitation. These methods do not model relationships between each pair of source and target such that they do not well capture the underlying interactions in the data. In this paper, we propose a new model for truth discovery, learning the representations of sources and claims automatically from the interactions between sources and targets. Our model first constructs a heterogenous network including source-claim, source-source and truth-claim relationships. It then embeds the network into a low dimensional space such that trustworthy sources and true claims are close. In this way, truth discovery can be conveniently performed in the embedding space. Moreover, our model can be implemented in both semi-supervised and un-supervised manners to deal with the label scarcity problem in practical truth discovery. Experiments on three real-world datasets demonstrate that our model outperforms existing state-of-the-art methods for truth discovery.

Answer selection is an important problem in community question answering (CQA), as it enables the distilling of reliable information and knowledge. Most existing approaches tackle this problem as a text matching task. However, they ignore the influence of the community in voting the best answers. Answer quality is highly correlated with semantic relevance and user expertise in CQA. In this paper, we formalize the answer selection problem from the user expertise view, considering both the semantic relevance in question-answer pair and user expertise in question-user pair. We design a novel matching function, explicitly modeling the influence of user expertise in community acceptance. Moreover, we introduce latent user vectors into the representation learning of answer, capturing the implicit topic interests in learned user vectors. Extensive experiments on two datasets from real world CQA sites demonstrate that our model outperforms state-of-the-art approaches for answer selection in CQA. Furthermore, the user representations learned by our model provide us a quantitative way to understand both the authority and topic-sensitive interests of users.

Users are usually involved in multiple social networks, without explicit anchor links that reveal the correspondence among different accounts of the same user across networks. Anchor link prediction aims to identify the hidden anchor links, which is a fundamental problem for user profiling, information cascading, and cross-domain recommendation. Although existing methods perform well in the accuracy of anchor link prediction, the pairwise search manners on inferring anchor links suffer from big challenge when being deployed in practical systems. To combat the challenges, in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node. Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction. Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction.