arXiv - CS - Social and Information Networks最新文献_第8页

LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning LATEX-GCL：基于大语言模型（LLMs）的文本归因图对比学习数据扩展

arXiv - CS - Social and Information Networks Pub Date : 2024-09-02 DOI: arxiv-2409.01145

Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, Guandong Xu

{"title":"LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning","authors":"Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, Guandong Xu","doi":"arxiv-2409.01145","DOIUrl":"https://doi.org/arxiv-2409.01145","url":null,"abstract":"Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised\u0000graph learning that has attracted attention across various application\u0000scenarios. However, GCL for learning on Text-Attributed Graphs (TAGs) has yet\u0000to be explored. Because conventional augmentation techniques like feature\u0000embedding masking cannot directly process textual attributes on TAGs. A naive\u0000strategy for applying GCL to TAGs is to encode the textual attributes into\u0000feature embeddings via a language model and then feed the embeddings into the\u0000following GCL module for processing. Such a strategy faces three key\u0000challenges: I) failure to avoid information loss, II) semantic loss during the\u0000text encoding phase, and III) implicit augmentation constraints that lead to\u0000uncontrollable and incomprehensible results. In this paper, we propose a novel\u0000GCL framework named LATEX-GCL to utilize Large Language Models (LLMs) to\u0000produce textual augmentations and LLMs' powerful natural language processing\u0000(NLP) abilities to address the three limitations aforementioned to pave the way\u0000for applying GCL to TAG tasks. Extensive experiments on four high-quality TAG\u0000datasets illustrate the superiority of the proposed LATEX-GCL method. The\u0000source codes and datasets are released to ease the reproducibility, which can\u0000be accessed via this link: https://anonymous.4open.science/r/LATEX-GCL-0712.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning 当异质性遇到异质图：潜在图引导的无监督表征学习

arXiv - CS - Social and Information Networks Pub Date : 2024-09-01 DOI: arxiv-2409.00687

Zhixiang Shen, Zhao Kang

{"title":"When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning","authors":"Zhixiang Shen, Zhao Kang","doi":"arxiv-2409.00687","DOIUrl":"https://doi.org/arxiv-2409.00687","url":null,"abstract":"Unsupervised heterogeneous graph representation learning (UHGRL) has gained\u0000increasing attention due to its significance in handling practical graphs\u0000without labels. However, heterophily has been largely ignored, despite its\u0000ubiquitous presence in real-world heterogeneous graphs. In this paper, we\u0000define semantic heterophily and propose an innovative framework called Latent\u0000Graphs Guided Unsupervised Representation Learning (LatGRL) to handle this\u0000problem. First, we develop a similarity mining method that couples global\u0000structures and attributes, enabling the construction of fine-grained homophilic\u0000and heterophilic latent graphs to guide the representation learning. Moreover,\u0000we propose an adaptive dual-frequency semantic fusion mechanism to address the\u0000problem of node-level semantic heterophily. To cope with the massive scale of\u0000real-world data, we further design a scalable implementation. Extensive\u0000experiments on benchmark datasets validate the effectiveness and efficiency of\u0000our proposed framework. The source code and datasets have been made available\u0000at https://github.com/zxlearningdeep/LatGRL.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Anti-Money Laundering Efforts with Network-Based Algorithms 利用基于网络的算法加强反洗钱工作

arXiv - CS - Social and Information Networks Pub Date : 2024-09-01 DOI: arxiv-2409.00823

Anthony Bonato, Juan Sebastian Chavez Palan, Adam Szava

{"title":"Enhancing Anti-Money Laundering Efforts with Network-Based Algorithms","authors":"Anthony Bonato, Juan Sebastian Chavez Palan, Adam Szava","doi":"arxiv-2409.00823","DOIUrl":"https://doi.org/arxiv-2409.00823","url":null,"abstract":"The global banking system has faced increasing challenges in combating money\u0000laundering, necessitating advanced methods for detecting suspicious\u0000transactions. Anti-money laundering (or AML) approaches have often relied on\u0000predefined thresholds and machine learning algorithms using flagged transaction\u0000data, which are limited by the availability and accuracy of existing datasets.\u0000In this paper, we introduce a novel algorithm that leverages network analysis\u0000to detect potential money laundering activities within large-scale transaction\u0000data. Utilizing an anonymized transactional dataset from Co\"operatieve\u0000Rabobank U.A., our method combines community detection via the Louvain\u0000algorithm and small cycle detection to identify suspicious transaction patterns\u0000below the regulatory reporting thresholds. Our approach successfully identifies\u0000cycles of transactions that may indicate layering steps in money laundering,\u0000providing a valuable tool for financial institutions to enhance their AML\u0000efforts. The results suggest the efficacy of our algorithm in pinpointing\u0000potentially illicit activities that evade current detection methods.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Faster Graph Partitioning via Pre-training and Inductive Inference 通过预训练和归纳推理实现更快的图谱划分

arXiv - CS - Social and Information Networks Pub Date : 2024-09-01 DOI: arxiv-2409.00670

Meng Qin, Chaorui Zhang, Yu Gao, Yibin Ding, Weipeng Jiang, Weixi Zhang, Wei Han, Bo Bai

{"title":"Towards Faster Graph Partitioning via Pre-training and Inductive Inference","authors":"Meng Qin, Chaorui Zhang, Yu Gao, Yibin Ding, Weipeng Jiang, Weixi Zhang, Wei Han, Bo Bai","doi":"arxiv-2409.00670","DOIUrl":"https://doi.org/arxiv-2409.00670","url":null,"abstract":"Graph partitioning (GP) is a classic problem that divides the node set of a\u0000graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge\u0000and recent advances in pre-training techniques (e.g., large-language models),\u0000we propose PR-GPT (Pre-trained & Refined Graph ParTitioning) based on a novel\u0000pre-training & refinement paradigm. We first conduct the offline pre-training\u0000of a deep graph learning (DGL) model on small synthetic graphs with various\u0000topology properties. By using the inductive inference of DGL, one can directly\u0000generalize the pre-trained model (with frozen model parameters) to large graphs\u0000and derive feasible GP results. We also use the derived partition as a good\u0000initialization of an efficient GP method (e.g., InfoMap) to further refine the\u0000quality of partitioning. In this setting, the online generalization and\u0000refinement of PR-GPT can not only benefit from the transfer ability regarding\u0000quality but also ensure high inference efficiency without re-training. Based on\u0000a mechanism of reducing the scale of a graph to be processed by the refinement\u0000method, PR-GPT also has the potential to support streaming GP. Experiments on\u0000the Graph Challenge benchmark demonstrate that PR-GPT can ensure faster GP on\u0000large-scale graphs without significant quality degradation, compared with\u0000running a refinement method from scratch. We will make our code public at\u0000https://github.com/KuroginQin/PRGPT.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GSpect: Spectral Filtering for Cross-Scale Graph Classification GSpect：跨尺度图分类的频谱过滤

arXiv - CS - Social and Information Networks Pub Date : 2024-08-31 DOI: arxiv-2409.00338

Xiaoyu Zhang, Wenchuan Yang, Jiawei Feng, Bitao Dai, Tianci Bu, Xin Lu

{"title":"GSpect: Spectral Filtering for Cross-Scale Graph Classification","authors":"Xiaoyu Zhang, Wenchuan Yang, Jiawei Feng, Bitao Dai, Tianci Bu, Xin Lu","doi":"arxiv-2409.00338","DOIUrl":"https://doi.org/arxiv-2409.00338","url":null,"abstract":"Identifying structures in common forms the basis for networked systems design\u0000and optimization. However, real structures represented by graphs are often of\u0000varying sizes, leading to the low accuracy of traditional graph classification\u0000methods. These graphs are called cross-scale graphs. To overcome this\u0000limitation, in this study, we propose GSpect, an advanced spectral graph\u0000filtering model for cross-scale graph classification tasks. Compared with other\u0000methods, we use graph wavelet neural networks for the convolution layer of the\u0000model, which aggregates multi-scale messages to generate graph representations.\u0000We design a spectral-pooling layer which aggregates nodes to one node to reduce\u0000the cross-scale graphs to the same size. We collect and construct the\u0000cross-scale benchmark data set, MSG (Multi Scale Graphs). Experiments reveal\u0000that, on open data sets, GSpect improves the performance of classification\u0000accuracy by 1.62% on average, and for a maximum of 3.33% on PROTEINS. On MSG,\u0000GSpect improves the performance of classification accuracy by 15.55% on\u0000average. GSpect fills the gap in cross-scale graph classification studies and\u0000has potential to provide assistance in application research like diagnosis of\u0000brain disease by predicting the brain network's label and developing new drugs\u0000with molecular structures learned from their counterparts in other systems.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social MediARverse Investigating Users Social Media Content Sharing and Consuming Intentions with Location-Based AR 利用基于位置的增强现实技术调查用户的社交媒体内容分享和消费意愿

arXiv - CS - Social and Information Networks Pub Date : 2024-08-30 DOI: arxiv-2409.00211

Linda Hirsch, Florian Müller, Mari Kruse, Andreas Butz, Robin Welsch

引用次数: 0

How Many Lines to Paint the City: Exact Edge-Cover in Temporal Graphs 用多少条线描绘城市？时态图中的精确边缘覆盖

arXiv - CS - Social and Information Networks Pub Date : 2024-08-30 DOI: arxiv-2408.17107

Argyrios Deligkas, Michelle Döring, Eduard Eiben, Tiger-Lily Goldsmith, George Skretas, Georg Tennigkeit

{"title":"How Many Lines to Paint the City: Exact Edge-Cover in Temporal Graphs","authors":"Argyrios Deligkas, Michelle Döring, Eduard Eiben, Tiger-Lily Goldsmith, George Skretas, Georg Tennigkeit","doi":"arxiv-2408.17107","DOIUrl":"https://doi.org/arxiv-2408.17107","url":null,"abstract":"Logistics and transportation networks require a large amount of resources to\u0000realize necessary connections between locations and minimizing these resources\u0000is a vital aspect of planning research. Since such networks have dynamic\u0000connections that are only available at specific times, intricate models are\u0000needed to portray them accurately. In this paper, we study the problem of\u0000minimizing the number of resources needed to realize a dynamic network, using\u0000the temporal graphs model. In a temporal graph, edges appear at specific points\u0000in time. Given a temporal graph and a natural number k, we ask whether we can\u0000cover every temporal edge exactly once using at most k temporal journeys; in a\u0000temporal journey consecutive edges have to adhere to the order of time. We\u0000conduct a thorough investigation of the complexity of the problem with respect\u0000to four dimensions: (a) whether the type of the temporal journey is a walk, a\u0000trail, or a path; (b) whether the chronological order of edges in the journey\u0000is strict or non-strict; (c) whether the temporal graph is directed or\u0000undirected; (d) whether the start and end points of each journey are given or\u0000not. We almost completely resolve the complexity of all these problems and\u0000provide dichotomies for each one of them with respect to k.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLMs hallucinate graphs too: a structural perspective 法学硕士也会产生图形幻觉：结构视角

arXiv - CS - Social and Information Networks Pub Date : 2024-08-30 DOI: arxiv-2409.00159

Erwan Le Merrer, Gilles Tredan

引用次数: 0

Service-Oriented AoI Modeling and Analysis for Non-Terrestrial Networks 面向服务的非地面网络 AoI 建模与分析

arXiv - CS - Social and Information Networks Pub Date : 2024-08-30 DOI: arxiv-2408.17051

Zheng Guo, Qian Chen, Weixiao Meng

{"title":"Service-Oriented AoI Modeling and Analysis for Non-Terrestrial Networks","authors":"Zheng Guo, Qian Chen, Weixiao Meng","doi":"arxiv-2408.17051","DOIUrl":"https://doi.org/arxiv-2408.17051","url":null,"abstract":"To achieve truly seamless global intelligent connectivity, non-terrestrial\u0000networks (NTN) mainly composed of low earth orbit (LEO) satellites and drones\u0000are recognized as important components of the future 6G network architecture.\u0000Meanwhile, the rapid advancement of the Internet of Things (IoT) has led to the\u0000proliferation of numerous applications with stringent requirements for timely\u0000information delivery. The Age of Information (AoI), a critical performance\u0000metric for assessing the freshness of data in information update systems, has\u0000gained significant importance in this context. However, existing modeling and\u0000analysis work on AoI mainly focuses on terrestrial networks, and the\u0000distribution characteristics of ground nodes and the high dynamics of\u0000satellites have not been fully considered, which poses challenges for more\u0000accurate evaluation. Against this background, we model the ground nodes as a\u0000hybrid distribution of Poisson point process (PPP) and Poisson cluster process\u0000(PCP) to capture the impact of ground node distribution on the AoI of status\u0000update packet transmission supported by UAVs and satellites in NTN, and the\u0000visibility and cross-traffic characteristics of satellites are additionally\u0000considered. We derived the average AoI for the system in these two different\u0000situations and examined the impact of various network parameters on AoI\u0000performance.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longitudinal Modularity, a Modularity for Link Streams 纵向模块化，链接流模块化

arXiv - CS - Social and Information Networks Pub Date : 2024-08-29 DOI: arxiv-2408.16877

Victor Brabant, Yasaman Asgari, Pierre Borgnat, Angela Bonifati, Remy Cazabet

引用次数: 0