arXiv - CS - Social and Information Networks最新文献_第5页

Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs 为稀疏标记图中的节点分类生成虚拟节点

arXiv - CS - Social and Information Networks Pub Date : 2024-09-12 DOI: arxiv-2409.07712

Hang Cui, Tarek Abdelzaher

{"title":"Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs","authors":"Hang Cui, Tarek Abdelzaher","doi":"arxiv-2409.07712","DOIUrl":"https://doi.org/arxiv-2409.07712","url":null,"abstract":"In the broader machine learning literature, data-generation methods\u0000demonstrate promising results by generating additional informative training\u0000examples via augmenting sparse labels. Such methods are less studied in graphs\u0000due to the intricate dependencies among nodes in complex topology structures.\u0000This paper presents a novel node generation method that infuses a small set of\u0000high-quality synthesized nodes into the graph as additional labeled nodes to\u0000optimally expand the propagation of labeled information. By simply infusing\u0000additional nodes, the framework is orthogonal to the graph learning and\u0000downstream classification techniques, and thus is compatible with most popular\u0000graph pre-training (self-supervised learning), semi-supervised learning, and\u0000meta-learning methods. The contribution lies in designing the generated node\u0000set by solving a novel optimization problem. The optimization places the\u0000generated nodes in a manner that: (1) minimizes the classification loss to\u0000guarantee training accuracy and (2) maximizes label propagation to\u0000low-confidence nodes in the downstream task to ensure high-quality propagation.\u0000Theoretically, we show that the above dual optimization maximizes the global\u0000confidence of node classification. Our Experiments demonstrate statistically\u0000significant performance improvements over 14 baselines on 10 publicly available\u0000datasets.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214681","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

Learning Personalized Scoping for Graph Neural Networks under Heterophily 嗜异性条件下的图神经网络个性化范围学习

arXiv - CS - Social and Information Networks Pub Date : 2024-09-11 DOI: arxiv-2409.06998

Gangda Deng, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna

{"title":"Learning Personalized Scoping for Graph Neural Networks under Heterophily","authors":"Gangda Deng, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna","doi":"arxiv-2409.06998","DOIUrl":"https://doi.org/arxiv-2409.06998","url":null,"abstract":"Heterophilous graphs, where dissimilar nodes tend to connect, pose a\u0000challenge for graph neural networks (GNNs) as their superior performance\u0000typically comes from aggregating homophilous information. Increasing the GNN\u0000depth can expand the scope (i.e., receptive field), potentially finding\u0000homophily from the higher-order neighborhoods. However, uniformly expanding the\u0000scope results in subpar performance since real-world graphs often exhibit\u0000homophily disparity between nodes. An ideal way is personalized scopes,\u0000allowing nodes to have varying scope sizes. Existing methods typically add\u0000node-adaptive weights for each hop. Although expressive, they inevitably suffer\u0000from severe overfitting. To address this issue, we formalize personalized\u0000scoping as a separate scope classification problem that overcomes GNN\u0000overfitting in node classification. Specifically, we predict the optimal GNN\u0000depth for each node. Our theoretical and empirical analysis suggests that\u0000accurately predicting the depth can significantly enhance generalization. We\u0000further propose Adaptive Scope (AS), a lightweight MLP-based approach that only\u0000participates in GNN inference. AS encodes structural patterns and predicts the\u0000depth to select the best model for each node's prediction. Experimental results\u0000show that AS is highly flexible with various GNN architectures across a wide\u0000range of datasets while significantly improving accuracy.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214679","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

DisasterNeedFinder: Understanding the Information Needs in the 2024 Noto Earthquake (Comprehensive Explanation) DisasterNeedFinder：了解 2024 年能登地震的信息需求（综合说明）

arXiv - CS - Social and Information Networks Pub Date : 2024-09-11 DOI: arxiv-2409.07102

Kota Tsubouchi, Shuji Yamaguchi, Keijirou Saitou, Akihisa Soemori, Masato Morita, Shigeki Asou

{"title":"DisasterNeedFinder: Understanding the Information Needs in the 2024 Noto Earthquake (Comprehensive Explanation)","authors":"Kota Tsubouchi, Shuji Yamaguchi, Keijirou Saitou, Akihisa Soemori, Masato Morita, Shigeki Asou","doi":"arxiv-2409.07102","DOIUrl":"https://doi.org/arxiv-2409.07102","url":null,"abstract":"We propose and demonstrate the DisasterNeedFinder framework in order to\u0000provide appropriate information support for the Noto Peninsula Earthquake. In\u0000the event of a large-scale disaster, it is essential to accurately capture the\u0000ever-changing information needs. However, it is difficult to obtain appropriate\u0000information from the chaotic situation on the ground. Therefore, as a\u0000data-driven approach, we aim to pick up precise information needs at the site\u0000by integrally analyzing the location information of disaster victims and search\u0000information. It is difficult to make a clear estimation of information needs by\u0000just analyzing search history information in disaster areas, due to the large\u0000amount of noise and the small number of users. Therefore, the idea of assuming\u0000that the magnitude of information needs is not the volume of searches, but the\u0000degree of abnormalities in searches, enables an appropriate understanding of\u0000the information needs of the disaster victims. DNF has been continuously\u0000clarifying the information needs of disaster areas since the disaster strike,\u0000and has been recognized as a new approach to support disaster areas by being\u0000featured in the major Japanese media on several occasions.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226900","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

Mapping the Russian Internet Troll Network on Twitter using a Predictive Model 利用预测模型绘制推特上的俄罗斯互联网巨魔网络图

arXiv - CS - Social and Information Networks Pub Date : 2024-09-11 DOI: arxiv-2409.08305

Sachith Dassanayaka, Ori Swed, Dimitri Volchenkov

{"title":"Mapping the Russian Internet Troll Network on Twitter using a Predictive Model","authors":"Sachith Dassanayaka, Ori Swed, Dimitri Volchenkov","doi":"arxiv-2409.08305","DOIUrl":"https://doi.org/arxiv-2409.08305","url":null,"abstract":"Russian Internet Trolls use fake personas to spread disinformation through\u0000multiple social media streams. Given the increased frequency of this threat\u0000across social media platforms, understanding those operations is paramount in\u0000combating their influence. Using Twitter content identified as part of the\u0000Russian influence network, we created a predictive model to map the network\u0000operations. We classify accounts type based on their authenticity function for\u0000a sub-sample of accounts by introducing logical categories and training a\u0000predictive model to identify similar behavior patterns across the network. Our\u0000model attains 88% prediction accuracy for the test set. Validation is done by\u0000comparing the similarities with the 3 million Russian troll tweets dataset. The\u0000result indicates a 90.7% similarity between the two datasets. Furthermore, we\u0000compare our model predictions on a Russian tweets dataset, and the results\u0000state that there is 90.5% correspondence between the predictions and the actual\u0000categories. The prediction and validation results suggest that our predictive\u0000model can assist with mapping the actors in such networks.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263487","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

A Novel Voting System for Medical Catalogues in National Health Insurance 全国医疗保险医疗目录的新型投票系统

arXiv - CS - Social and Information Networks Pub Date : 2024-09-11 DOI: arxiv-2409.07057

Xingyuan Liang, Haibao Wen

引用次数: 0

Market Reaction to News Flows in Supply Chain Networks 市场对供应链网络中新闻流的反应

arXiv - CS - Social and Information Networks Pub Date : 2024-09-10 DOI: arxiv-2409.06255

Hiroyasu Inoue, Yasuyuki Todo

{"title":"Market Reaction to News Flows in Supply Chain Networks","authors":"Hiroyasu Inoue, Yasuyuki Todo","doi":"arxiv-2409.06255","DOIUrl":"https://doi.org/arxiv-2409.06255","url":null,"abstract":"This study examines whether positive news about firms increases their stock\u0000prices and, moreover, whether it increases stock prices of the firms' suppliers\u0000and customers, using a large sample of publicly listed firms across the world\u0000and another of Japanese listed firms. The level of positiveness of each news\u0000article is determined by FinBERT, a natural language processing model\u0000fine-tuned specifically for financial information. Supply chains of firms\u0000across the world are identified mostly by financial statements, while those of\u0000Japanese firms are taken from large-scale firm-level surveys. We find that\u0000positive news increases the change rate of stock prices of firms mentioned in\u0000the news before its disclosure, most likely because of diffusion of information\u0000through informal channels. Positive news also raises stock prices of the firms'\u0000suppliers and customers before its disclosure, confirming propagation of market\u0000values through supply chains. In addition, we generally find a larger post-news\u0000effect on stock prices of the mentioned firms and their suppliers and customers\u0000than the pre-news effect. The positive difference between the post- and\u0000pre-news effects can be considered as the net effect of the disclosure of\u0000positive news, controlling for informal information diffusion. However, the\u0000post-news effect on suppliers and customers in Japan is smaller than the\u0000pre-news effect, a result opposite to those from firms across the world. This\u0000notable result is possibly because supply chain links of Japanese firms are\u0000stronger than global supply chains while such knowledge is restricted to\u0000selected investors.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214680","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

Fast Computation for the Forest Matrix of an Evolving Graph 快速计算演化图的森林矩阵

arXiv - CS - Social and Information Networks Pub Date : 2024-09-09 DOI: arxiv-2409.05503

Haoxin Sun, Xiaotian Zhou, Zhongzhi Zhang

{"title":"Fast Computation for the Forest Matrix of an Evolving Graph","authors":"Haoxin Sun, Xiaotian Zhou, Zhongzhi Zhang","doi":"arxiv-2409.05503","DOIUrl":"https://doi.org/arxiv-2409.05503","url":null,"abstract":"The forest matrix plays a crucial role in network science, opinion dynamics,\u0000and machine learning, offering deep insights into the structure of and dynamics\u0000on networks. In this paper, we study the problem of querying entries of the\u0000forest matrix in evolving graphs, which more accurately represent the dynamic\u0000nature of real-world networks compared to static graphs. To address the unique\u0000challenges posed by evolving graphs, we first introduce two approximation\u0000algorithms, textsc{SFQ} and textsc{SFQPlus}, for static graphs. textsc{SFQ}\u0000employs a probabilistic interpretation of the forest matrix, while\u0000textsc{SFQPlus} incorporates a novel variance reduction technique and is\u0000theoretically proven to offer enhanced accuracy. Based on these two algorithms,\u0000we further devise two dynamic algorithms centered around efficiently\u0000maintaining a list of spanning converging forests. This approach ensures $O(1)$\u0000runtime complexity for updates, including edge additions and deletions, as well\u0000as for querying matrix elements, and provides an unbiased estimation of forest\u0000matrix entries. Finally, through extensive experiments on various real-world\u0000networks, we demonstrate the efficiency and effectiveness of our algorithms.\u0000Particularly, our algorithms are scalable to massive graphs with more than\u0000forty million nodes.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214683","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

Fast Computation of Kemeny's Constant for Directed Graphs 快速计算有向图的凯美尼常数

arXiv - CS - Social and Information Networks Pub Date : 2024-09-09 DOI: arxiv-2409.05471

Haisong Xia, Zhongzhi Zhang

引用次数: 0

Extracting the U.S. building types from OpenStreetMap data 从 OpenStreetMap 数据中提取美国建筑类型

arXiv - CS - Social and Information Networks Pub Date : 2024-09-09 DOI: arxiv-2409.05692

Henrique F. de Arruda, Sandro M. Reia, Shiyang Ruan, Kuldip S. Atwal, Hamdi Kavak, Taylor Anderson, Dieter Pfoser

{"title":"Extracting the U.S. building types from OpenStreetMap data","authors":"Henrique F. de Arruda, Sandro M. Reia, Shiyang Ruan, Kuldip S. Atwal, Hamdi Kavak, Taylor Anderson, Dieter Pfoser","doi":"arxiv-2409.05692","DOIUrl":"https://doi.org/arxiv-2409.05692","url":null,"abstract":"Building type information is crucial for population estimation, traffic\u0000planning, urban planning, and emergency response applications. Although\u0000essential, such data is often not readily available. To alleviate this problem,\u0000this work creates a comprehensive dataset by providing\u0000residential/non-residential building classification covering the entire United\u0000States. We propose and utilize an unsupervised machine learning method to\u0000classify building types based on building footprints and available\u0000OpenStreetMap information. The classification result is validated using\u0000authoritative ground truth data for select counties in the U.S. The validation\u0000shows a high precision for non-residential building classification and a high\u0000recall for residential buildings. We identified various approaches to improving\u0000the quality of the classification, such as removing sheds and garages from the\u0000dataset. Furthermore, analyzing the misclassifications revealed that they are\u0000mainly due to missing and scarce metadata in OSM. A major result of this work\u0000is the resulting dataset of classifying 67,705,475 buildings. We hope that this\u0000data is of value to the scientific community, including urban and\u0000transportation planners.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214682","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

Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis Instagram 上的 Mpox 叙事：用于情感、仇恨言论和焦虑分析的 Mpox Instagram 帖子标签化多语言数据集

arXiv - CS - Social and Information Networks Pub Date : 2024-09-09 DOI: arxiv-2409.05292

Nirmalya Thakur

{"title":"Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis","authors":"Nirmalya Thakur","doi":"arxiv-2409.05292","DOIUrl":"https://doi.org/arxiv-2409.05292","url":null,"abstract":"The world is currently experiencing an outbreak of mpox, which has been\u0000declared a Public Health Emergency of International Concern by WHO. No prior\u0000work related to social media mining has focused on the development of a dataset\u0000of Instagram posts about the mpox outbreak. The work presented in this paper\u0000aims to address this research gap and makes two scientific contributions to\u0000this field. First, it presents a multilingual dataset of 60,127 Instagram posts\u0000about mpox, published between July 23, 2022, and September 5, 2024. The\u0000dataset, available at https://dx.doi.org/10.21227/7fvc-y093, contains Instagram\u0000posts about mpox in 52 languages. For each of these posts, the Post ID, Post\u0000Description, Date of publication, language, and translated version of the post\u0000(translation to English was performed using the Google Translate API) are\u0000presented as separate attributes in the dataset. After developing this dataset,\u0000sentiment analysis, hate speech detection, and anxiety or stress detection were\u0000performed. This process included classifying each post into (i) one of the\u0000sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or\u0000neutral, (ii) hate or not hate, and (iii) anxiety/stress detected or no\u0000anxiety/stress detected. These results are presented as separate attributes in\u0000the dataset. Second, this paper presents the results of performing sentiment\u0000analysis, hate speech analysis, and anxiety or stress analysis. The variation\u0000of the sentiment classes - fear, surprise, joy, sadness, anger, disgust, and\u0000neutral were observed to be 27.95%, 2.57%, 8.69%, 5.94%, 2.69%, 1.53%, and\u000050.64%, respectively. In terms of hate speech detection, 95.75% of the posts\u0000did not contain hate and the remaining 4.25% of the posts contained hate.\u0000Finally, 72.05% of the posts did not indicate any anxiety/stress, and the\u0000remaining 27.95% of the posts represented some form of anxiety/stress.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214714","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