2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)最新文献_第5页

Two phase transitions in the adaptive voter model based on the homophily principle 基于同质性原则的自适应选民模型的两个相变

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921579

Takashi Ishikawa

引用次数: 0

On the endogenesis of Twitter's Spritzer and Gardenhose sample streams 论推特的Spritzer和Gardenhose样本流的内生

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921610

Dennis Kergl, R. Roedler, Sebastian Seeber

{"title":"On the endogenesis of Twitter's Spritzer and Gardenhose sample streams","authors":"Dennis Kergl, R. Roedler, Sebastian Seeber","doi":"10.1109/ASONAM.2014.6921610","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921610","url":null,"abstract":"Many recent publications deal with trend analysis, event detection or opinion mining on social media data. Twitter, as the most important microblogging service, is often in the focus of these works, as it offers free access to big volumes of data. The free access, on that many publications rely, is composed of a random subset of the complete public status stream. Publications rely particularly on the uniform distribution of tweets in this sample stream, and therefore, till today, one has to trust in the statement of Twitter that the sample data is indeed uniformly distributed1. In our research on the technical properties of Twitter's streaming data, we found evidence for discovering the method used by Twitter to decide which tweets will show up in the random sample streams. A deeper insight into this process leads to the possible reasons of why Twitter chose the presented sampling method. For this purpose we provide an overview of how Twitter's unique tweet IDs are generated and explain the regularities of each part of a tweet ID. This results also in some information about the tweet ID generating infrastructure of Twitter and what kind of knowledge can possibly be derived from small features like the tweet ID.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114357424","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: 35

Understanding South Asian Violent Extremist Group-group interactions 了解南亚暴力极端主义团体-团体间的互动

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921660

D. Skillicorn, Francesca Spezzano, V. S. Subrahmanian, M. Garber

引用次数: 7

Using triads to identify local community structure in social networks 利用三合会识别社会网络中的本地社区结构

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921568

Justin Fagnan, Osmar R Zaiane, Denilson Barbosa

引用次数: 17

Gossip-based partitioning and replication for Online Social Networks 基于八卦的在线社交网络分区和复制

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921557

Muhammad Anis Uddin Nasir, Fatemeh Rahimian, Sarunas Girdzijauskas

{"title":"Gossip-based partitioning and replication for Online Social Networks","authors":"Muhammad Anis Uddin Nasir, Fatemeh Rahimian, Sarunas Girdzijauskas","doi":"10.1109/ASONAM.2014.6921557","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921557","url":null,"abstract":"Online Social Networks (OSNs) have been gaining tremendous growth and popularity in the last decade, as they have been attracting billions of users from all over the world. Such networks generate petabytes of data from the social interactions among their users and create many management and scalability challenges. OSN users share common interests and exhibit strong community structures, which create complex dependability patterns within OSN data, thus, make it difficult to partition and distribute in a data center environment. Existing solutions, such as, distributed databases, key-value stores and auto scaling services use random partitioning to distribute the data across a cluster, which breaks existing dependencies of the OSN data and may generate huge inter-server traffic. Therefore, there is a need for intelligent data allocation strategy that can reduce the network cost for various OSN operations. In this paper, we present a gossip-based partitioning and replication scheme that efficiently splits OSN data and distributes the data across a cluster. We achieve fault tolerance and data locality, for one-hop neighbors, through replication. Our main contribution is a social graph placement strategy that divides the social graph into predefined size partitions and periodically updates the partitions to place socially connected users together. To evaluate our algorithm, we compare it with random partitioning and a state-of-the-art solution SPAR. Results show that our algorithm generates up to four times less replication overhead compared to random partitioning and half the replication overhead compared to SPAR.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126921408","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: 5

Research on the selecting mechanism of individual interactive object for online social network 在线社交网络中个体交互对象选择机制研究

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921681

Yu-Xiao Pang, Shiyu Du

{"title":"Research on the selecting mechanism of individual interactive object for online social network","authors":"Yu-Xiao Pang, Shiyu Du","doi":"10.1109/ASONAM.2014.6921681","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921681","url":null,"abstract":"The exploration of the mechanism of human behavior becomes very hot and interesting due to its importance and complexity. With the improvement of network technology, online social networks have developed rapidly and changed the traditional way of people's communication. Large amount of data of user behavior is recorded, which gives us an opportunity to study the mechanism of human behavior. In this paper, we mainly research on the selecting mechanism of individual on online social networks. First of all, we put forward the individual interactive model used for explaining how an individual choose his interactive object on online social network based on the sociology, psychology and network theory. Then, to fit the data requirement of this model, we crawl and collect the data from actual online platforms. Furthermore, simulation and empirical study have been completed in two different platforms respectively to verify the accuracy of this model. Finally, we gained high level prediction accuracy rate to predict people's interactive behavior in future periods, which brings great significance in relative study area.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151168","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

Question difficulty evaluation by knowledge gap analysis in Question Answer communities 基于知识缺口分析的问答社区问题难度评价

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921606

Chih-Lu Lin, Ying-Liang Chen, Hung-Yu kao

{"title":"Question difficulty evaluation by knowledge gap analysis in Question Answer communities","authors":"Chih-Lu Lin, Ying-Liang Chen, Hung-Yu kao","doi":"10.1109/ASONAM.2014.6921606","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921606","url":null,"abstract":"The Community Question Answer (CQA) service is a typical forum of Web 2.0 that shares knowledge among people. There are thousands of questions that are posted and solved every day. Because of the various users of the CQA service, question search and ranking are the most important topics of research in the CQA portal. In this study, we addressed the problem of identifying questions as being hard or easy by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and used a knowledge gap diagram to illustrate how much of a knowledge gap exists in different categories. To this end, we proposed an approach called the knowledge-gap-based difficulty rank (KG-DRank) algorithm, which combines the user-user network and the architecture of the CQA service to find hard questions. We used f-measure, AUC, MAP, NDCG, precision@Top5 and concordance analysis to evaluate the experimental results. Our results show that our approach leads to better performance than other baseline approaches across all evaluation metrics.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128100493","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: 10

A fuzzy clustering algorithm to detect criminals without prior information 一种无先验信息的模糊聚类算法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921590

Changjun Fan, Kaiming Xiao, Bao-Xin Xiu, Guodong Lv

引用次数: 9

A novel algorithm for community detection and influence ranking in social networks 一种新的社交网络社区检测与影响力排序算法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921641

Wenjun Wang, W. Street

引用次数: 27

An analysis of positivity and negativity attributes of users in twitter 推特用户的积极与消极属性分析

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921611

M. Roshanaei, Shivakant Mishra

{"title":"An analysis of positivity and negativity attributes of users in twitter","authors":"M. Roshanaei, Shivakant Mishra","doi":"10.1109/ASONAM.2014.6921611","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921611","url":null,"abstract":"Effect of mood and emotion on a person's behavior and his/her interactions with other people has been studied for a long time. Positivity and negativity of a person are two important attributes of emotion and mood. Social media is a very important platform from which we can glean the positivity and negativity attributes of a user based on his/her message postings and interactions with other users. In this paper, we study and analyze a Twitter dataset of more than 130,000 users to understand the nature of their positivity and negativity attributes. We measure behavioral attributes by sentiment analysis relating to social personal concern and psychological process. We observe that social media contains useful behavioral cues to classify users into positive and negative groups based on network density and degree of social activity either in information sharing or emotional interaction and social awareness. We believe that our findings will be useful in developing tools for predicting positive and negative users and help provide the best recommendation towards helping negative users through online social media.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122062117","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: 11