Social Network Analysis and Mining最新文献

Applying a panel network formation model to limited partnership matching in the private capital market. 面板网络形成模型在私人资本市场有限合伙企业匹配中的应用。

IF 2.8

Social Network Analysis and Mining Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1007/s13278-025-01527-7

Yuanyuan Shang, Philip Leifeld

引用次数: 0

Assessing mobile instant messenger networks with donated data. 利用捐赠数据评估移动即时通讯网络。

IF 2.8

Social Network Analysis and Mining Pub Date : 2026-01-01 Epub Date: 2025-12-27 DOI: 10.1007/s13278-025-01550-8

Rense Corten, Laura Boeschoten, Thijs Carrière, Stein Jongerius, Bella Struminskaya, Joris Mulder, Parisa Zahedi, Shiva Nadi Najafabadi, Adriënne Mendrik

{"title":"Assessing mobile instant messenger networks with donated data.","authors":"Rense Corten, Laura Boeschoten, Thijs Carrière, Stein Jongerius, Bella Struminskaya, Joris Mulder, Parisa Zahedi, Shiva Nadi Najafabadi, Adriënne Mendrik","doi":"10.1007/s13278-025-01550-8","DOIUrl":"10.1007/s13278-025-01550-8","url":null,"abstract":"Despite the increasing popularity and societal relevance of mobile instant messenger services (MIMSs) such as WhatsApp, Telegram and Signal, the vast majority of the research on social media is focused on the \"traditional\" social media platforms such as Twitter (X) and Facebook, mostly due to the difficulty of accessing MIMSs data. Consequently, little is known about even the most basic topological features of societal-scale instant messenger networks. To overcome this knowledge gap, we employ the innovative approach of data donation by respondents of a high-quality probability-based panel in the Netherlands (the LISS panel) to collect user data while preserving their privacy. Focusing on WhatsApp as the most popular MIMS, this study collects the first measurement of MIMS usage on a national probability sample, focusing on degree distributions as a key feature of network topology. We find that the degree distribution of contacts is best approximated by a log-normal distribution, while the distribution of group membership is best approximated by the exponential distribution. At the individual level, we find that predictors of degree derived from the literature on extended networks mostly replicate.Supplementary information: The online version contains supplementary material available at 10.1007/s13278-025-01550-8.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"16 1","pages":"23"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12819457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data. C-HDNet：一种基于快速超维计算的网络观测数据因果效应估计方法。

IF 3.2

Social Network Analysis and Mining Pub Date : 2025-01-01 Epub Date: 2025-10-24 DOI: 10.1007/s13278-025-01502-2

Abhishek Dalvi, Neil Ashtekar, Vasant G Honavar

{"title":"C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data.","authors":"Abhishek Dalvi, Neil Ashtekar, Vasant G Honavar","doi":"10.1007/s13278-025-01502-2","DOIUrl":"10.1007/s13278-025-01502-2","url":null,"abstract":"We address the problem of estimating causal effects from observational data in the presence of network confounding, a setting where both treatment assignment and observed outcomes of individuals may be influenced by their neighbors within a network structure, resulting in network interference. Traditional causal inference methods often fail to account for these dependencies, leading to biased estimates. To tackle this challenge, we introduce a novel matching-based approach that utilizes principles from hyperdimensional computing to effectively encode and incorporate structural network information. This enables more accurate identification of comparable individuals, thereby improving the reliability of causal effect estimates. Through extensive empirical evaluation on multiple benchmark datasets, we demonstrate that our method either outperforms or performs on par with existing state-of-the-art approaches, including several recent deep learning-based models that are significantly more computationally intensive. In addition to its strong empirical performance, our method offers substantial practical advantages, achieving nearly an order-of-magnitude reduction in runtime without compromising accuracy, making it particularly well-suited for large-scale or time-sensitive applications.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"15 1","pages":"97"},"PeriodicalIF":3.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12552378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145378738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian mixture model for Poisson network autoregression. 泊松网络自回归的贝叶斯混合模型。

IF 2.8

Social Network Analysis and Mining Pub Date : 2025-01-01 Epub Date: 2025-07-17 DOI: 10.1007/s13278-025-01485-0

Elly Hung, Anastasia Mantziou, Gesine Reinert

{"title":"A Bayesian mixture model for Poisson network autoregression.","authors":"Elly Hung, Anastasia Mantziou, Gesine Reinert","doi":"10.1007/s13278-025-01485-0","DOIUrl":"10.1007/s13278-025-01485-0","url":null,"abstract":"Multivariate count time series arise in a wide range of applications, including the number of COVID-19 cases recorded each week in different counties of the Republic of Ireland. In this example, it is natural to view the counties as nodes in a network, with edges between counties reflecting proximity. One could then model disease spread on a network through a regression model. Often Gaussian errors are assumed for such a model, but for count data this assumption may not be natural. With this motivating example in mind, we develop a model with the following features. We assume that the time series occur on the nodes of a known underlying network where the edges dictate the form of a structural vector autoregression model. In contrast to using a full vector autoregressive model, the network assumption is a means of imposing sparsity. Moreover we aim for a model that is able to accommodate heterogeneous node dynamics, and to cluster nodes that exhibit similar behaviour. To address these aims, we propose a new Bayesian Poisson network autoregression mixture model that we call a PNARM model, which combines ideas from Poisson network autoregression models, grouped network autoregression models, and non-uniform co-clustering priors.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"15 1","pages":"70"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing minority stress detection with transformers: insights from the social media datasets. 用变压器推进少数民族应力检测：来自社交媒体数据集的见解。

IF 3.2

Social Network Analysis and Mining Pub Date : 2025-01-01 Epub Date: 2025-09-03 DOI: 10.1007/s13278-025-01521-z

Santosh Chapagain, Cory J Cascalheira, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Jillian R Scheer

{"title":"Advancing minority stress detection with transformers: insights from the social media datasets.","authors":"Santosh Chapagain, Cory J Cascalheira, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Jillian R Scheer","doi":"10.1007/s13278-025-01521-z","DOIUrl":"10.1007/s13278-025-01521-z","url":null,"abstract":"Individuals from sexual and gender minority groups experience disproportionately high rates of poor health outcomes and mental disorders compared to their heterosexual and cisgender counterparts, largely as a consequence of minority stress as described by Meyer's (Psychol Bull 129(5):674, 2003) model. This study presents the first comprehensive evaluation of transformer-based architectures for detecting minority stress in online discourse. We benchmark multiple transformer models including ELECTRA, BERT, RoBERTa, and BART against traditional machine learning baselines and graph-augmented variants. We further assess zero-shot and few-shot learning paradigms to assess their applicability on underrepresented datasets. Experiments are conducted on the two largest publicly available Reddit corpora for minority stress detection, comprising 12,645 and 5789 posts, and are repeated over five random seeds to ensure robustness. Our results demonstrate that integrating graph structure consistently improves detection performance across transformer-only models and that supervised fine-tuning with relational context outperforms zero and few-shot approaches. Theoretical analysis reveals that modeling social connectivity and conversational context via graph augmentation sharpens the models' ability to identify key linguistic markers such as identity concealment, internalized stigma, and calls for support, suggesting that graph-enhanced transformers offer the most reliable foundation for digital health interventions and public health policy.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"15 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach 更正：摩洛哥公众对可再生能源的看法：利用基于规则的方法进行民意挖掘

IF 2.8

Social Network Analysis and Mining Pub Date : 2024-01-13 DOI: 10.1007/s13278-023-01191-9

M. Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, A. Beni-Hssane

引用次数: 0

Do users adopt extremist beliefs from exposure to hate subreddits? 用户是否会因为接触仇恨子论坛而接受极端主义信仰？

IF 2.8

Social Network Analysis and Mining Pub Date : 2024-01-11 DOI: 10.1007/s13278-023-01184-8

Matheus Schmitz, Goran Muric, Daniel Hickey, Keith Burghardt

引用次数: 0

Comparing methods for creating a national random sample of twitter users. 比较创建全国随机twitter用户样本的方法。

IF 2.3

Social Network Analysis and Mining Pub Date : 2024-01-01 Epub Date: 2024-08-14 DOI: 10.1007/s13278-024-01327-5

Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi

{"title":"Comparing methods for creating a national random sample of twitter users.","authors":"Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi","doi":"10.1007/s13278-024-01327-5","DOIUrl":"10.1007/s13278-024-01327-5","url":null,"abstract":"Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and friends, a fewer number of likes, are younger accounts, and include more male users compared to the other three methods. Moreover, it achieves the minimum error in estimating the US population. However, the 1% Stream method is time-consuming, cannot be used for the past time frames, and is not suitable when user engagement is part of the study. In situation where these three drawbacks are important, our results support the Bounding Box method as the second-best method.Supplementary information: The online version contains supplementary material available at. 10.1007/s13278-024-01327-5.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"14 1","pages":"160"},"PeriodicalIF":2.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis 利用先进的情感分析机器学习，分析泰国-中国高速列车和老挝-中国铁路大型项目的网络舆情

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-18 DOI: 10.1007/s13278-023-01168-8

Manussawee Nokkaew, K. Nongpong, Tapanan Yeophantong, Pattravadee Ploykitikoon, W. Arjharn, A. Siritaratiwat, Sorawit Narkglom, W. Wongsinlatam, T. Remsungnen, A. Namvong, C. Surawanitkun

引用次数: 0

Semantic overlapping community detection with embedding multi-dimensional relationships and spatial context 嵌入多维关系和空间背景的语义重叠群落检测

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-17 DOI: 10.1007/s13278-023-01173-x

Shulin Cheng, Shan Yang, Xiufang Cheng, Keyu Li, Yu Zheng

引用次数: 0