The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs; (2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural Networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score.

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.

With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.

Trajectory clustering is an essential task in spatial data mining. To address this problem, many previous studies either extended traditional clustering algorithms with spatial features of trajectories or employed deep learning models for representation learning. However, one common drawback of existing solutions is that the final number of clusters needs to be specified as part of the input. In this paper, we proposed Tra-jDPM, an end-to-end framework for non-parametric trajectory clustering. We come up with two novel loss functions to pretrain a trajectory encoder so as to generate discriminative trajectory representation. Moreover, we employed the neural Dirichlet process mixture model to perform non-parametric clustering based on trajectory embeddings. In this process, the trajectory encoder can also be jointly optimized to improve the performance by a contrastive learning based strategy. We conduct an extensive set of evaluations on several public datasets. Experimental results show that our proposed framework can outperform state-of-the-art methods by a significant margin.

Multimodal deep learning technologies have advanced significantly, which brings extensive applications in diverse fields. The substantial computational demands of training and prediction in multimodal deep learning have made the End-Edge-Cloud (EEC) framework popular. It is essential to protect multimodal data and model privacy in such a framework. However, traditional cryptographic methods, though secure for data and models at edge nodes, cause efficiency limitations. In this paper, we propose SecMdp, an SGX-assisted secure computational framework for multimodal data in the EEC architecture. Edge nodes are equipped with the trusted execution environment (e.g., Intel SGX) to run multimodal algorithms. Additionally, to address the side-channel attacks of SGX, we present an enhanced PathORAM algorithm, MM_PathORAM, for the multimodal training and prediction processes, which are tailored for multimodal deep learning scenarios. It accelerates multimodal data access while protecting data privacy and model security. Experimental evaluation supports the effectiveness of our design in preserving edge computing efficiency. It demonstrates negligible impact on the speed of multimodal data loading, the configuration of model parameters during training, or the accuracy of predictions.

Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for com-munities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to heterogeneous information networks (HINs) composed of multi-typed vertices and different semantic relations, such as bibliographic networks. In this paper, we study the problem of high-importance community search in HINs. A novel community model is introduced, named heterogeneous significant community (HSC), to unravel the closely connected vertices of the same type with high attribute values through multiple semantic relationships. An HSC not only maximizes the exploration of indirect relationships across entities of the anchor-type but incorporates their significance. To search the HSCs, we first develop online algorithms by exploiting both segmented-based meta-path expansion and significance incrernent. Specially, a solution space reuse strategy based on structural nesting is designed to boost the efficiency. In addition, we further devise a two-level index to support searching HSCs in optimal time, based on which a space-efficient compact index is proposed. Extensive experiments on real-world large-scale HINs demonstrate that our solutions are effective and efficient for searching HSCs, and the index-based algorithms are 2–4 orders of magnitude faster than online algorithms.

Federated learning has emerged as a promising paradigm for large-scale collaborative training tasks, harnessing diverse local datasets from different clients to jointly train global models. In real-world implementations, client data could have label noise, causing the quality of the global model to be influenced. Existing label-correction solutions assume all the clients are discreet and fail to consider detecting the malicious clients, thus are not practical or privacy-preserving. In this paper, we present zkCor, an efficient and reliable label noise correction scheme with zero-knowledge confidentiality. Our method is designed upon FedCorr [1], but with more relaxed security assumptions. zkCor is established from the ingenious synergy of the label noise correction protocol and the zero-knowledge proof (ZKP), requiring each client to provide a computation integrity proof to the aggregator in each iteration. Thus, clients are forced to jointly guarantee label-correction reliability. We further devise a batch ZKP that is efficient and more suitable for federated learning settings. We rigorously illustrate the building blocks of zkCor and complete the prototype implementation. The extensive experiments demonstrate that zkCor can gain at least 2 to 30 times better performance than the baseline approach on verification workloads with nearly no extra proof time cost from clients.

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve traveling to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition and a task allocation phase. The first phase divides the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points, and workers. To further improve the efficiency, we propose an early stopping optimization strategy for the adaptive weighted Voronoi diagram-based algorithm in the geographic partition phase and give a distance-constrained graph pruning strategy for the Reinforcement Learning method in the task allocation phase. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.

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.

Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.

Autonomous driving is a rapidly advancing field that promises to revolutionize the transportation industry through an intelligent perception-and-decision paradigm. Despite decades of research, existing methods are limited in adapting to complex scenarios or expanding to unseen situations, which pose significant challenges to the development of autonomous driving. Inspired by the process of human learning to drive, autonomous vehicles can prioritize developing driving capabilities in basic scenarios and then extending the atomic abilities to more complex scenarios. To this end, we proposed a perception-and-decision framework, called ATEND, which consists of an adaptive perception module and a maneuver decision module. Specifically, the perception module based on Variational Autoencoder is proposed to map perceptual data of complex scenarios into basic scenarios. Then the reinforcement learning-based decision module can make high-level decisions in transformed scenarios. Once ATEND learns to drive in basic scenarios, it can achieve safe and efficient driving in real scenarios without additional training. Extensive experiments in different traffic scenarios evidence that the proposed framework advances the state of the art in terms of both macroscopic and microscopic effectiveness.

The importance of social bot detection has been increasingly recognized due to its profound impact on information dissemination. Existing methodologies can be categorized into feature engineering and deep learning-based methods, which mainly focus on static features, e.g., post characteristics and user profiles.However, existing methods often overlook the burst phenomena when distinguishing social bots and genuine users, i.e, the sudden and intense activity or behavior of bots after prolonged inter. Through comprehensive analysis, we find that both burst behavior and static features play pivotal roles in social bot detection. To capture such properties, the dual-channel GNN (DCGNN) is proposed which consists of a burst-aware channel with an adaptive-pass filter and a static-aware channel with a low-pass filter to model user characteristics effectively. Experimental results demonstrate the superiority of this method over competitive baselines.

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.

With the widespread development of decentralized storage, it is increasingly popular for users to store their data to the decentralized database systems for the well-understood benefits of outsourced storage. To ensure the data privacy, systems commonly require users to securely keep their private keys. Thus, the secure storage of private keys is an important issue in these systems. However, existing key-management schemes commonly rely on a Trusted Third Party (TTP), which raises critical security concerns such as the single point of failure and Distributed Denial of Service (DDoS) attacks. In this paper, we propose HasDPSS, a secure and efficient blockchain-based key-management scheme for decentralized storage systems. It uses secret sharing, a lightweight cryptographic technique, to build the decentralized key-management scheme. Considering that the reliability of managing participants has inherent heterogeneity, we introduce the hierarchical access structure to achieve fine-grained key management. Meanwhile, to adapt the node churn of decentralized key management, HasDPSS enables a dynamic management committee to provide reliable services with a proactive refresh mechanism while protecting the integrity and security of private keys. In our design, we use the dimension switch method of polynomials in the evolving process to achieve the committee change of the hierarchical access structure. The reliability of participants is guaranteed by the customized commitment protocol and the immutable property of the blockchain. We thoroughly analyze security strengths and conduct extensive experiments to demonstrate the practicality of our design.

Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user preferences? Typically, in the face of such a problem setting, we can model the network as a multi-attributed road-social network, in which each user is linked with location information and d (≥1) numerical attributes. In practice, user preferences (i.e., weights) are usually inherently uncertain and can only be estimated with bounded accuracy, because a human user is not able to designate exact values with absolute precision. Inspired by this, we introduce a normative community model suitable for multi-criteria decision making, called multi-attributed community (MAC), based on the concepts of k-core and a novel dominance relationship specific to preferences. Given uncertain user preferences, namely, an approximate representation of weights, the MAC search reports the exact communities for each of the possible weight settings. We devise an elegant index structure to maintain the dominance relationships, based on which two algorithms are developed to efficiently compute the top-j MACs. The efficiency and scalability of our algorithms and the effectiveness of MAC model are demonstrated by extensive experiments on both real-world and synthetic road-social networks.

The group nearest neighbor (GNN) search on a road network$G_r$, i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on$G_r$, has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top-$j$objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top$j$are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top-$j$objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

The group nearest neighbor (GNN) search on a road network Gr, i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on Gr, has been extensively studied; however, previous works have neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study a new problem: a GNN search on a road network that incorporates cohesive social relationships (CGNN). Specifically, both the query users of highest closeness and the corresponding top-j objects are retrieved. One critical challenge is to speed up the computation of CGNN queries over large social and road networks. To address this challenge, we propose a filtering-and-verification framework for efficient query processing. During filtering, we prune substantial unpromising users and objects using social and geographically spatial constraints. During verification, we obtain the object candidates, among which the top j are selected, with respect to the qualified users. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.