2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第9页

Hackers Hedging Bets: A Cross-Community Analysis of Three Online Hacking Forums 黑客对冲赌注:三个在线黑客论坛的跨社区分析

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508613

Andrew J. Park, Richard Frank, A. Mikhaylov, Myfanwy Thomson

引用次数: 10

Analyzing Social and Communication Network Structures of Social Bots and Humans 分析社会机器人和人类的社会和通信网络结构

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508665

Tuja Khaund, Kiran Kumar Bandeli, Muhammad Nihal Hussain, A. Obadimu, Samer Al-khateeb, Nitin Agarwal

{"title":"Analyzing Social and Communication Network Structures of Social Bots and Humans","authors":"Tuja Khaund, Kiran Kumar Bandeli, Muhammad Nihal Hussain, A. Obadimu, Samer Al-khateeb, Nitin Agarwal","doi":"10.1109/ASONAM.2018.8508665","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508665","url":null,"abstract":"Recently, several journalistic accounts have suggested that Twitter is becoming a bellwether for mis- and disinformation due to the pervasiveness of bots. These bots are either automated or semi-automated. Understanding the intent and usage of these bots has piqued the scientific curiosity among researchers. To that effect, in this study, we analyze the role of bots in two distinct categories of real-world events, i.e., natural disasters and sports. We collected over 1.2 million tweets that were generated by nearly 800,000 users for Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake. We corroborate our analysis by examining bots that engaged with the 2018 Winter Olympics. We collected over 1.4 million tweets generated by nearly 700,000 users based on the hashtags #Olympics2018 and #PyeongChang2018. We examined the social and communication network of bots and humans for the aforementioned events. Our results show distinctive patterns in the network structures of bots when compared with that of humans. Content analysis of the tweets further revealed that bots used hashtags more uniformly than humans, across all the events.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752872","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: 4

ALGeoSPF: A Hierarchical Factorization Model for POI Recommendation 基于层次分解的POI推荐模型

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508249

Jean-Benoît Griesner, T. Abdessalem, Hubert Naacke, Pierre Dosne

{"title":"ALGeoSPF: A Hierarchical Factorization Model for POI Recommendation","authors":"Jean-Benoît Griesner, T. Abdessalem, Hubert Naacke, Pierre Dosne","doi":"10.1109/ASONAM.2018.8508249","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508249","url":null,"abstract":"The task of points-of-interest (POI) recommendations has become an essential feature in location-based social networks (LBSNs) with the significant growth of shared data on LBSNs. However it remains a challenging problem, because the decision process of a user choosing to visit a POI depends on numerous factors. The high level of sparsity of the data in LBSNs makes the POI recommendation problem even more challenging, especially for large geographical areas and worldwide datasets. Moreover, in this context the mobility behavior of the users is very heterogeneous, ranging from urban to worldwide mobility. In this paper, we explore the impact of spatial clustering on the recommendation quality. The proposed approach combines spatial clustering with users' influences. It is based on a Poisson factorization model built on an implicit social network, inferred from the geographical mobility patterns. We conduct a comprehensive performance evaluation of our approach on the YFCC dataset (a very large-scale real-world dataset). The experiments show that our approach achieves a significantly superior recommendation quality compared to other state-of-the-art recommendation techniques.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125010769","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: 2

Measuring the Information-Foraging Behaviors of Social Bots Through Word Usage 通过词汇使用来衡量社交机器人的信息采集行为

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508811

Zachary Kimo Stine, Tuja Khaund, Nitin Agarwal

引用次数: 4

AI Robo-Advisor with Big Data Analytics for Financial Services 金融服务大数据分析AI机器人顾问

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508854

Min-Yuh Day, Tun-Kung Cheng, Jheng-Gang Li

{"title":"AI Robo-Advisor with Big Data Analytics for Financial Services","authors":"Min-Yuh Day, Tun-Kung Cheng, Jheng-Gang Li","doi":"10.1109/ASONAM.2018.8508854","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508854","url":null,"abstract":"Robo-Advisors has been growing attraction from the financial industry for offering financial services by using algorithms and acting as like human advisors to support investors making investment decisions. During the investment planning stage, portfolio optimization plays a crucial role, especially for the medium and long-term investors, in determining the allocation weight of assets to achieve the balance between investors expectation return and risk tolerance. The literature on the topic of portfolio optimization has been offering plenty of theoretical and practical guidance for implementing the theory; however, there is a paucity of studies focusing on the applications which are designed for Robo-Advisors. In this research, we proposed a modular system and focused on integrating big data analysis, deep learning method and the Black-Litterman model to generate asset allocation weight. We developed a portfolio optimization module which takes the information from a variety of sources, such as stocks prices, investor profile and the other alternative data, and used them as input to calculate optimal weights of assets in the portfolio. The module we developed could be used as a sub-system for Robe-Advisors, which offers a customized optimal portfolio based on investors preference.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129166872","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: 13

Individual-Level Social Capital in Weighted and Attributed Social Networks 加权和归因社会网络中的个人层面社会资本

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508302

Rajesh Sharma, Kevin McAreavey, Jun Hong, Faisal Ghaffar

引用次数: 3

Interactive Kernel Dimension Alternative Clustering on GPUs gpu上的交互式内核维度可选聚类

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508264

Xiangyu Li, Chieh Wu, Shi Dong, Jennifer G. Dy, D. Kaeli

{"title":"Interactive Kernel Dimension Alternative Clustering on GPUs","authors":"Xiangyu Li, Chieh Wu, Shi Dong, Jennifer G. Dy, D. Kaeli","doi":"10.1109/ASONAM.2018.8508264","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508264","url":null,"abstract":"Machine learning has seen tremendous growth in recent years thanks to two key advances in technology: massive data generation and highly-parallel accelerator architectures. The rate that data is being generated is exploding across multiple domains, including medical research, environmental science, web-search, and e-commerce. Many of these advances have benefited from emergent web-based applications, and improvements in data storage and sensing technologies. Innovations in parallel accelerator hardware, such as GPUs, has made it possible to process massive amounts of data in a timely fashion. Given these advanced data acquisition technology and hardware, machine learning researchers are equipped to generate and sift through much larger and complex datasets quickly. In this work, we focus on accelerating Kernel Dimension Alternative Clustering algorithms using GPUs. We conduct a thorough performance analysis by using both synthetic and real-world datasets, while also modifying both the structure of the data, and the size of the datasets. Our GPU implementation reduces execution time from minutes to seconds, which enables us to develop a web-based application for users to, interactively, view alternative clustering solutions.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126435630","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

End-to-End Compromised Account Detection 端到端入侵账户检测

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508296

Hamid Karimi, Courtland VanDam, L. Ye, Jiliang Tang

{"title":"End-to-End Compromised Account Detection","authors":"Hamid Karimi, Courtland VanDam, L. Ye, Jiliang Tang","doi":"10.1109/ASONAM.2018.8508296","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508296","url":null,"abstract":"Social media, e.g. Twitter, has become a widely used medium for the exchange of information, but it has also become a valuable tool for hackers to spread misinformation through compromised accounts. Hence, detecting compromised accounts is a necessary step toward a safe and secure social media environment. Nevertheless, detecting compromised accounts faces several challenges. First, social media activities of users are temporally correlated which plays an important role in compromised account detection. Second, data associated with social media accounts is inherently sparse. Finally, social contagions where multiple accounts become compromised, take advantage of the user connectivity to propagate their attack. Thus how to represent each user's network features for compromised account detection is an additional challenge. To address these challenges, we propose an End-to-End Compromised Account Detection framework (E2ECAD). E2ECAD effectively captures temporal correlations via an LSTM (Long Short-Term Memory) network. Further, it addresses the sparsity problem by defining and employing a user context representation. Meanwhile, informative network-related features are modeled efficiently. To verify the working of the framework, we construct a real-world dataset of compromised accounts on Twitter and conduct extensive experiments. The results of experiments show that E2ECAD outperforms the state of the art compromised account detection algorithms.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652619","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: 23

A Statistical Framework for Handling Network Anomalies 网络异常处理的统计框架

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508299

M. Bouguessa, Amani Chouchane

引用次数: 0

A Model of Homophily, Common Knowledge and Collective Action Through Facebook 通过Facebook的同质性、共同知识和集体行动模型

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508834

Gizem Korkmaz, C. Kuhlman, Joshua Goldstein, F. Vega-Redondo

引用次数: 5