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

Big Data in Health Informatics Architecture 健康信息架构中的大数据

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-10-16 DOI: 10.1109/ASONAM.2014.6921667

E. R. Onyejekwe

引用次数: 4

ASONAM 2014 tutorials [2 abstracts] ASONAM 2014教程[2个摘要]

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-10-16 DOI: 10.1109/ASONAM.2014.6921549

Hsun-Ping Hsieh, Cheng-te Li, C. Tan

引用次数: 0

Recommendation in Academia: A joint multi-relational model 学术界的建议:联合多关系模型

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921643

Zaihan Yang, Dawei Yin, Brian D. Davison

引用次数: 22

Identifying relevant event content for real-time event detection 识别相关事件内容，进行实时事件检测

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921616

Xinyue Wang, L. Tokarchuk, S. Poslad

引用次数: 16

Behavioral detection of spam URL sharing: Posting patterns versus click patterns 垃圾URL共享的行为检测:发布模式与点击模式

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921573

C. Cao, James Caverlee

{"title":"Behavioral detection of spam URL sharing: Posting patterns versus click patterns","authors":"C. Cao, James Caverlee","doi":"10.1109/ASONAM.2014.6921573","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921573","url":null,"abstract":"Social media systems like Twitter and Facebook provide a global infrastructure for sharing information, and in one popular direction, of sharing web hyperlinks. Understanding the behavioral signals of both how URLs are inserted into these systems (via posting by users) and how URLs are received by social media users (via clicking) can provide new insights into social media search, recommendation, and user profiling, among many others. Such studies, however, have traditionally been difficult due to the proprietary (and sometimes private) nature of much URL-related data. Hence, in this paper, we begin a behavioral examination of URL sharing through two distinct perspectives: (i) the first is via a study of how these links are posted through publicly-accessible Twitter data; (ii) the second is via a study of how these links are received by measuring their click patterns through the publicly-accessible Bitly click API. We examine the differences between posting and click patterns in a sample application domain: the classification of spam URLs. We find that these behavioral signals - posting versus clicking - provide overlapping but fundamentally different perspectives on URLs, and that these perspectives can inform the design of future applications of spam link detection and link sharing.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"150 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":"114444395","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: 18

Location inference using microblog text and friendships 利用微博文本和友情进行位置推断

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921674

Chuanyang Li, Xiuqin Lin, Bin Wu, C. Shi

引用次数: 0

An modularity-based overlapping community structure detecting algorithm 一种基于模块化的重叠社团结构检测算法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921569

Kui Meng, Gongshen Liu, Qiong Hu, Jianhua Li

引用次数: 3

Cluster cascades: Infer multiple underlying networks using diffusion data 集群级联:使用扩散数据推断多个底层网络

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921597

Ming Yang, C. Chou, Ming-Syan Chen

{"title":"Cluster cascades: Infer multiple underlying networks using diffusion data","authors":"Ming Yang, C. Chou, Ming-Syan Chen","doi":"10.1109/ASONAM.2014.6921597","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921597","url":null,"abstract":"Information diffusion and virus propagation are the fundamental processes often taking place in networks. The problem of devising a strategy to facilitate or block such process has received a considerable amount of attention. A major challenge therein is that the underlying network of diffusion is often hidden. Most researchers dealing with this issue assume only one underlying network over which cascades spread. However, in the real world, whether the transmission pathways of a contagion, a piece of information, emerge or not depends on many factors, such as the topic of the information and the time when the information is first mentioned. In our opinion, it is impractical to model the diffusion processes by using only a single network when information is of all kind and diffuses in different underlying topic-specific networks. In this paper, we formulate a problem of K-network inference, inferring K underlying diffusion networks, based on a proposed probabilistic generative mixture model that models the generation of cascades. We further propose an algorithm that could cluster similar cascades and infer the corresponding underlying network for each cluster in the Expectation-Maximization framework. Finally, in experiments, we show that our algorithm could cluster cascades and infer the underlying networks effectively.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"14 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":"129122492","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

Learning the information diffusion probabilities by using variance regularized EM algorithm 利用方差正则化EM算法学习信息扩散概率

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921596

Hai-Guang Li, Tianyu Cao, Zhao Li

{"title":"Learning the information diffusion probabilities by using variance regularized EM algorithm","authors":"Hai-Guang Li, Tianyu Cao, Zhao Li","doi":"10.1109/ASONAM.2014.6921596","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921596","url":null,"abstract":"In this paper we address the problem of learning the information diffusion probabilities when there is no sufficient data of information diffusion. By observing the information diffusion behavior on the popular social network web-site Twitter, we find that the evidence of information diffusion is extremely sparse. Less than one percent of tweets are retweeted, which is considered as the most important form of information diffusion evidence on Twitter. Previous research on predicting information diffusion probabilities has failed under such scenarios because the problem of over fitting. To overcome this problem, we first propose to use the variance of the diffusion probabilities as a measure of model complexity for the independent cascade model. After that, we propose two regularization schemes to reduce model complexity. The first scheme is based on regularizing the variance of the diffusion probabilities directly. The second scheme is based on regularizing the mean absolute deviation of the logarithm of the diffusion probabilities. We are able to derive an approximation solution for the first scheme and analytical solution to the second scheme. We conduct experiments by simulating information diffusion on six social network datasets. Experimental results show that the variance regularization scheme outperforms the baseline by a noticeable margin. The mean absolute deviation regularization scheme is better than the baseline.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"41 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":"124108296","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

Scanning network communities with power-law-distributed attributes 扫描具有幂律分布属性的网络社区

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921584

Tai-Chi Wang, F. Phoa

引用次数: 2