2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第2页

Using Synthetic Data to Reduce Model Convergence Time in Federated Learning 利用综合数据减少联邦学习中的模型收敛时间

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068615

F. Dankar, N. Madathil

{"title":"Using Synthetic Data to Reduce Model Convergence Time in Federated Learning","authors":"F. Dankar, N. Madathil","doi":"10.1109/ASONAM55673.2022.10068615","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068615","url":null,"abstract":"Federated Learning (FL) is a hot new topic in collaborative training of machine learning problems. It is a privacy-preserving distributed machine learning approach, allowing multiple clients to jointly train a global model under the coordination of a central server, while keeping their sensitive data private. The problem with FL systems is that they require intense communication between the server and clients to achieve the final machine learning model. Such complexity increases with the number of clients participating and the complexity of the model sought. In this paper, we introduce synthetic data generation into FL systems with the intention of reducing the number of iterations required for model convergence. In this novel method, clients generate synthetic datasets modeling their private data. The synthetic datasets are then sent to the central server and are used to generate a cognizant initial model. Our experiments show that such conscious method for generating the initial model lowers the number of iterations by a factor of more than 4 without affecting the model accuracy. As such it enhances the overall efficiency of FL systems.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"420 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131752360","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: 1

Visualization of Influential Blog Networks Using BlogTracker 使用BlogTracker可视化有影响力的博客网络

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068720

Abiola Akinnubi, Nitin Agarwal, Ayokunle Sunmola, Vanessa Okeke

{"title":"Visualization of Influential Blog Networks Using BlogTracker","authors":"Abiola Akinnubi, Nitin Agarwal, Ayokunle Sunmola, Vanessa Okeke","doi":"10.1109/ASONAM55673.2022.10068720","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068720","url":null,"abstract":"The advent of web 2.0 and social media blogging has enabled researchers to have access to troves of blog data in the 21st century. While platforms and big corporations like Twitter and Facebook can apply the concept of user networks by leveraging internal tools developed by their teams. Researchers and analysts have had to make use of repetitive ways of analyzing blog networks for collected data. This is due to the limited live databases to store and keep track of blog data and the lack of centralized publicly available tools with such capability. When analyzing blog data, the analyst often wants the capability to model relationships and see blogs that share ideological similarities. This is so because blogs always reference each other when they share similarities in content or when they attempt to reinforce a point of view discussed on the medium. Since the blogosphere is made up of a virtual network of blogs - the blogosphere is defined as the network of blogs and has no limitation in blogs referencing one another. It becomes imperative to have a solution that can allow an analyst to visualize the relationships between blogs based on how influential these blogs are when the analyst tracks the discus on the blogs. We address this by providing users with the capability to visualize and analyze blogs that are influential and how connected these blogs are by a way of network visualization. This demonstration shows how the BlogTracker application analyze and visualizes the blog network.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130898843","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

Portuguese Twitter Dataset on COVID-19 关于COVID-19的葡萄牙语推特数据集

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068592

R. A. A. Jonker, Roshan Poudel, Olga Fajarda, Sérgio Matos, J. L. Oliveira, Rui Pedro Lopes

{"title":"Portuguese Twitter Dataset on COVID-19","authors":"R. A. A. Jonker, Roshan Poudel, Olga Fajarda, Sérgio Matos, J. L. Oliveira, Rui Pedro Lopes","doi":"10.1109/ASONAM55673.2022.10068592","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068592","url":null,"abstract":"Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformatics-ua/Portuguese-Covid19-Dataset","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130984250","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

PLMP: A Method to Map the Linguistic Markers of the Social Discourse onto its Semantic Network PLMP:一种将社会话语的语言标记映射到其语义网络的方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068643

T. Erseghe, L. Badia, Lejla Dzanko, Caterina Suitner

引用次数: 2

Text Mining with Information Extraction for Chinese Financial Knowledge Graph 基于信息抽取的中文金融知识图谱文本挖掘

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068569

Yung-Wei Teng, Min-Yuh Day, Pei-Tz Chiu

{"title":"Text Mining with Information Extraction for Chinese Financial Knowledge Graph","authors":"Yung-Wei Teng, Min-Yuh Day, Pei-Tz Chiu","doi":"10.1109/ASONAM55673.2022.10068569","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068569","url":null,"abstract":"Financial Documents reveal important financial information about a company's financial performance which plays a vital role not only to the stakeholders but also to the public. Therefore, many researchers utilize dynamic Text mining methods in financial document to identify, analyze, predict or evaluate a company's future financial value. In order to find deeply the relationship between companies and the stakeholders, provide a simplified method for them to identify the future financial performance of the corporation. In this paper, we present a Chinese Information Extraction System (CFIES) for Financial Knowledge Graph (FinKG). The major findings of the research show an increased importance of the key audit matters in finance. The major research contribution of this paper is that we have developed CFIES which can extract the tuples from the financial reports. The adoption of the information system can assist the development of a knowledge graph that can discover deep financial knowledge in the finance domain. The managerial implication is that building CFIES can efficiently enable us to clarify the complicated relationship between the corporations, board of directors, investors, and especially the asset, assisting the stakeholders to discover a new financial knowledge representation and to make a financial decision.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133257315","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

PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit Reddit上的半监督深度学习阿片类药物危机检测

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068721

Abdulaziz Alhamadani, Shailik Sarkar, Lulwah Alkulaib, Chang-Tien Lu

引用次数: 1

Multi-relational Affinity Propagation 多关系亲和传播

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068598

Hossam Sharara, L. Getoor

{"title":"Multi-relational Affinity Propagation","authors":"Hossam Sharara, L. Getoor","doi":"10.1109/ASONAM55673.2022.10068598","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068598","url":null,"abstract":"There is a growing need for clustering algorithms which can operate in complex settings where there are multiple entity types with potential dependencies captured in different kinds of links. In this work, we present a novel approach for multi-relational clustering based on both the similarity of the entities' features, along with the multi-relational structure of the network among the entities. Our approach extends the affinity propagation clustering algorithm to multi-relational domains and encodes a variety of relational constraints to capture the dependencies across different node types in the underlying network. In contrast to the original formulation of affinity propagation that relies on enforcing hard constraints on the output clusters, we model the relational dependencies as soft constraints, allowing control over how they influence the final clustering of the nodes. This formulation allows us to balance between the homogeneity of the entities within the resulting clusters and their connections to clusters of nodes of the same and differing types. This in turn facilitates the exploration of the middle ground between feature-based similarity clustering, community detection, and block modeling in multi-relational networks. We present results on clustering a sample from Digg.com, a richly structured online social news website. We show that our proposed algorithm outperforms other clustering approaches on a variety of evaluation measures. We also analyze the impact of different parameter settings on the clustering output, in terms of both the homogeneity and the connectedness of the resulting clusters.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116959240","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

Investigating Community Detection in Arabic Scholarly Network Using Ontology-based Semantic Expansion 基于本体的语义扩展研究阿拉伯语学术网络中的社区检测

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068618

Sarah Al-Shareef, Rahaf Alharbi, Rawan Alharbi, Raghad Almfarriji, Maram Alsharif, Rasha Alharthi, Lamia Althaqafi

{"title":"Investigating Community Detection in Arabic Scholarly Network Using Ontology-based Semantic Expansion","authors":"Sarah Al-Shareef, Rahaf Alharbi, Rawan Alharbi, Raghad Almfarriji, Maram Alsharif, Rasha Alharthi, Lamia Althaqafi","doi":"10.1109/ASONAM55673.2022.10068618","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068618","url":null,"abstract":"Clustering researchers in communities is an important task to support a range of techniques for analyzing and making sense of the research environment and helps re-searchers find people in the same field of interest to collaborate. In computer science, ontology is commonly used to capture knowledge about a particular area using relevant concepts and relations. This study investigates the use of overlapping community detection algorithms on a multilayered Arabic scholarly network to detect communities of researchers who share their research interests. Two researchers can share an interest if they co-authored a publication or share some keywords in their publications. The set of keywords is expanded via semantic search within a cross-domain ontology, e.g. DBpedia, allowing more researchers with indirect relationships to be connected. A 2-layer scholarly network was constructed by retrieving the scholarly data of faculty members from three colleges at Umm AlQura University (UQU) with rich Arabic publications. Four versions of this network were tested: unweighted, weighted, semantically expanded, and reduced semantically expanded. It was found that weights have an insignificant role in community detection within this study. In addition, a semantically expanded network does have better clustering potentials but only if was performed selectively. Otherwise, the expanded network might suffer from generic and non-discriminative keywords, making the community detection task more challenging. To our knowledge, this is the first investigation into detecting communities within an Arabic scholarly network.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116009576","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: 1

Ukraine as a Political Tool in Facebook Sponsored Content 乌克兰作为Facebook赞助内容的政治工具

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068635

Oliver Melbourne Allen, F. Menczer

引用次数: 0

Welcome from FAB 2022 Chairs: International Symposium on Foundations and Applications of Big Data Analytics (FAB 2022) 主持人:大数据分析基础与应用国际研讨会(FAB 2022)

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/asonam55673.2022.10068641

引用次数: 0