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International Conference on Data Engineering

作者： Youhua Li Hanwen Du Yongxin Ni Pengpeng Zhao Qi Guo Fajie Yuan Xiaofang Zhou ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies SHIC Westlake University Hangzhou China School of Computer Science and Technology Soochow University Suzhou China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the de-signing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold -start issue. In this paper, we unleash the boundaries of the ID- based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. Specifically, we design a plug-and-play framework architecture consisting of multi-modal item encoders, a fusion module, and a user encoder. To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders. To ensure the robustness of user representations, we propose a novel noised item detection objective and a robustness-aware contrastive learning objective, which work together to denoise user sequences in a self-supervised manner. PMMRec is designed to be loosely coupled, so after being pre-trained on the source data, each component can be transferred alone, or in conjunction with other components, allowing PMMRec to achieve versatility under both multi-modality and single-modality transfer learning settings. Extensive experiments on 4 sources and 10 target datasets demonstrate that PMMRec surpasses the state-of-the-art recommenders in both recommendation performance and transferability. Our code and dataset is available at: https://***/ICDE24IPMMRec.

关键词： Codes Transfer learning Noise Contrastive learning Data engineering Robustness Vectors

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Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck 39

Bi-Directional Multi-Scale Graph Dataset Condensation via In...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fu, Xingcheng Gao, Yisen Yang, Beining Wu, Yuxuan Qian, Haodong Sun, Qingyun Li, Xianxian Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)157735897X

Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data sizes. Thus, condensing multiple scale graphs simultaneously is the core of achieving efficient training in different on-device scenarios. Existing efficient works for multi-scale graph dataset condensation mainly perform efficient approximate computation in scale order (large-to-small or small-to-large scales). However, for non-Euclidean structures of sparse graph data, these two commonly used paradigms for multi-scale graph dataset condensation have serious scaling down degradation and scaling up collapse problems of a graph. The main bottleneck of the above paradigms is whether the effective information of the original graph is fully preserved when consenting to the primary sub-scale (the first of multiple scales), which determines the condensation effect and consistency of all scales. In this paper, we proposed a novel GNN-centric Bi-directional Multi-Scale Graph Dataset Condensation (BiMSGC) framework, to explore unifying paradigms by operating on both large-to-small and small-to-large for multi-scale graph condensation. Based on the mutual information theory, we estimate an optimal "meso-scale" to obtain the minimum necessary dense graph preserving the maximum utility information of the original graph, and then we achieve stable and consistent "bi-directional" condensation learning by optimizing graph eigenbasis matching with information bottleneck on other scales. Encouraging empirical results on several datasets demonstrates the significant superiority of the proposed framework in graph condensation at different scales. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large datasets

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PGUD: Personalized Graph Universal Defense

PGUD: Personalized Graph Universal Defense

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zeming Gan Linlin Su Xianxian Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

Extensive evidence shows that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Previous work has made great efforts to improve the performance and usability of GNNs, but they remain sensitive and vulnerable to local attacks. Local attacks slightly perturb the target nodes, resulting in misclassification of GNNs on these specific nodes. Graph Universal Defense (GUD) is proposed to generate a defense patch to protect important nodes in a graph from local attacks. However, the research on GUD is still in the preliminary stage. The unique patch does not adaptively protect different nodes, and it has weak robustness against strong local attacks. To address this problem, we propose Personalized Graph Universal Defense (PGUD). It maps nodes into several clusters, each corresponding to a personalized defense patch. We further instantiate a class-specific PGUD. From a perspective of effective perturbation at minimal cost, we theoretically derive the potential attacker nodes manipulated by the adversary, and use patches to mitigate their impact on target nodes. Experimental results show that our approach can be efficiently integrated into any GNN for downstream tasks and provide effective protection for different target nodes in a personalized manner. Our method significantly improves the robustness against local attacks compared to prior work and can be easily scaled to large graphs.

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Relaxed Graph Semi-Supervised Contrastive Learning for Node Classification

Relaxed Graph Semi-Supervised Contrastive Learning for Node ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Qiyu Li Xianxian Li Haodong Qian De Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

Graph Neural Networks (GNNs) have emerged as promising tools in graph semi-supervised learning. They acquire low-dimensional node embeddings for downstream tasks by aggregating and updating features from neighboring nodes. However, in a semi-supervised setting, the availability of labeled node information is limited, resulting in the underutilization of vast amounts of unlabeled node information. Current research integrates contrastive learning with graph semi-supervised learning to better use this unlabeled node information. Nevertheless, the differences between cross-entropy and self-supervised contrastive loss are often overlooked. To address this issue, we propose a novel relaxed graph contrastive semi-supervised learning method. We analyze the difference between nodes correctly classified under cross-entropy loss and self-supervised contrastive loss. Inspired by this, we design a relaxed contrastive loss that considers the node features optimized under these two loss functions, thereby expanding the positive sample set. Furthermore, to ensure the quality of the positive sample set, we introduce a threshold constraint based on feature similarity and prediction results to select more reliable positive samples. The experimental results indicate that our method exhibits competitive performance across most datasets.

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MedRE-DeBGPR: A Novel Model for Medical Relation Extraction Based on DeBERTa, GlobalPointer and RoPE

SSRN

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SSRN 2024年

作者： Deng, Zhiqi Luo, Xudong Gong, Shutao Liu, Jinlu School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

In medical text analysis, extracting relationships among medical entities is crucial for enhancing clinical decision-making and building comprehensive medical knowledge graphs. This paper proposes a novel model for Relation Extraction (RE) from medical texts, which combines the strengths of DeBERTa (a pre-trained language model known for capturing complex linguistic phenomena) and GPLinker (a framework adept at handling overlapping and nested entities). By integrating these two technologies, our model effectively manages the inherent complexities of medical language, such as domain-specific terminology, complex sentence structures, and nested relationships. The proposed model uses the Fast Gradient Method (FGM) for adversarial training to improve robustness, Efficient GlobalPointer for parameter usage optimisation, and Rotary Position Embedding (RoPE) for better entity recognition. Experimental results on the CMedCausal dataset demonstrate that our approach outperforms existing baseline models and large language models in extracting entity relationships from medical documents. This model has significant implications for developing clinical decision support systems and medical knowledge graphs, and it holds possibility for adaptation across different languages and medical domains. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Modeling and Performance Analysis of mmWave and WiFi Transmissions for V2V Communications

Modeling and Performance Analysis of mmWave and WiFi Transmi...

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IEEE Conference on Wireless Communications and Networking

作者： Mohamed Rjab Aymen Omri Seifeddine Bouallegue Hela Chamkhia Ridha Bouallegue National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Innovation of Communicant & Cooperative Mobiles (Innov'Com) Lab (SUP'COM) Higher School of Communications of Tunis University of Carthage Ariana Tunisia College of Computing and Information Technology University of Doha for Science and Technology Qatar Computer Science and Engineering Department Qatar University Doha Qatar

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Vehicle-to-Vehicle (V2V) communications are revolutionizing the connectivity of transportation systems, ensuring safe and efficient road mobility. To meet the growing bandwidth needs of V2V services, the use of millimeter-wave (mmWave) and WiFi frequency bands has been proposed as a promising solution. However, communications at these frequencies are a challenging task due to the severe path-loss especially in a high-blocked environment. In this paper, we present a comparative study between mmWave and WiFi communication systems. In particular, realistic stochastic geometry tools have been used to accurately model and describe a typical V2V communication systems, where vehicles are randomly located. Accordingly, we derive the corresponding Successful Transmission Probability (STP) and Average Throughput (AT) expressions for both communication systems. In addition, Monte Carlo simulations have been performed and presented to confirm the analytical results, and to investigate the advantages and disadvantages of the mmWaves and WiFi systems, in different scenarios.

关键词： Geometry Analytical models Communication systems Vehicular ad hoc networks Stochastic processes Transportation Throughput

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A KNOWLEDGE-BASED IDEATION APPROACH FOR BIO-INSPIRED DESIGN 24

A KNOWLEDGE-BASED IDEATION APPROACH FOR BIO-INSPIRED DESIGN

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24th International Conference on Engineering Design, ICED 2023

作者： Chen, Liuqing Cai, Zebin Jiang, Zhaojun Long, Qi Sun, Lingyun Childs, Peter Zuo, Haoyu Department of Computer Science and Technology Zhejiang University Hangzhou310030 China Singapore Innovation and Ai Joint Research Lab Zhejiang University Hangzhou310030 China School of Mechanical Engineering Tianjin University Tianjin300350 China Zhejiang University-University of Illinois at Urbana-Champaign Institute Haining314400 China Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom

Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. This paper proposes a knowledge-based approach to address the challenges. The approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. The structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. The knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Big data

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HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding 39

HeGTa: Leveraging Heterogeneous Graph-enhanced Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Rihui Li, Yu Qi, Guilin Hu, Nan Li, Yuan-Fang Chen, Jiaoyan Wang, Jianan Chen, Yongrui Min, Dehai Bi, Sheng School of Computer Science and Engineering Southeast University China Ministry of Education China Monash University Australia The University of Manchester United Kingdom Alibaba Group China Law and Innovation Lab Law School Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Table Understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HeGTa, a heterogeneous graph (HG)-enhanced large language model (LLM) designed for few-shot TU tasks. This framework aligns structural table semantics with the LLM's parametric knowledge through soft prompts and instruction tuning. It also addresses complex tables with a multi-task pre-training scheme by incorporating novel multi-granularity self-supervised HG pretext tasks. We empirically demonstrate the effectiveness of HeGTa, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural Networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

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Why blockchain Needs Graph: A Survey on Studies, Scenarios, and Solutions

SSRN

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SSRN 2023年

作者： Song, Jie Zhang, Pengyi Qu, Qiang Bai, Yongjie Gu, Yu Yu, Ge Software College Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen518055 China Blockchain Lab Huawei Cloud Tech Co. Ltd Shenzhen518101 China School of Computer Science and Engineering Northeastern University Shenyang110819 China

The popularity of blockchain platforms and their applications in industry and academia keeps rising. The multifarious requirements stimulate another technique, graph data and algorithms, to join the blockchains;thus, studies, scenarios, and solutions about graph-related blockchains have emerged. State-of-the-art studies have reported on ``blockchain + graph'' solutions, but few of them natively integrate graph computing into a blockchain. Here, the ``graph integration'' means processing on-chain graph data, which contains blockchain information, in a real-time, distributed, and consensual manner. Aiming to answer why a blockchain needs graphs, and why a Graph-integrated blockchain Platform (GiBP for short) is necessary and challengeable, we analyze literature about blockchain and graph, as well as use cases on the application-oriented and graph-related scenarios collected from practical blockchain projects. We draw the findings for three graph-related blockchain scenarios: graph algorithms for blockchains, graph data in blockchains, and graph applications on blockchains. Based on these findings, we explain why a GiBP is inevitable, the challenges of a GiBP, and the GiBPs’ main functions and features people expect. We also propose the prospection of a GiBP for future research. © 2023, The Authors. All rights reserved.

关键词： blockchain

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