2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)最新文献_第7页

Joint voting prediction for questions and answers in CQA CQA中问答的联合投票预测

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921607

Yuan Yao, Hanghang Tong, Tao Xie, L. Akoglu, Feng Xu, Jian Lu

引用次数: 9

PEVNET: A framework for visualization of criminal networks 犯罪网络可视化框架

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1145/3092090.3092098

Amer Rasheed, U. Wiil, M. Niazi

引用次数: 6

Community detection in dynamic social networks: A game-theoretic approach 动态社会网络中的社区检测:一种博弈论方法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921567

Hamidreza Alvari, Alireza Hajibagheri, G. Sukthankar

引用次数: 49

Automatic classification of scientific groups as productive: An approach based on motif analysis 基于基序分析的科学群体自动分类

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921572

Tanmoy Chakraborty, Niloy Ganguly, Animesh Mukherjee

{"title":"Automatic classification of scientific groups as productive: An approach based on motif analysis","authors":"Tanmoy Chakraborty, Niloy Ganguly, Animesh Mukherjee","doi":"10.1109/ASONAM.2014.6921572","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921572","url":null,"abstract":"One of the key aspects instrumental in the advancement of science relates to “team science,” or in other words “group” collaborations. There have been extensive studies analyzing various statistical properties of collaborations of individual or pairs of authors. However, the number of studies pertaining to groups/teams of scientists working together is limited in number. In this paper, we set an objective to study the productivity of group collaborations where groups are represented as small substructures usually termed as network motifs in the literature. A preliminary observation is that star-like motifs have the largest productivity (defined as a function of citation count) followed by 4-cliques. We then introduce a bunch of features and study their individual relations with the productivity of a team. Building on these observations, we develop a supervised classification model that can automatically distinguish the highly productive teams from the low productive ones based on the set of identified features. The accuracy of the classification is 82% on an average for all the motifs with the accuracy reaching as high as 95% for 4-cliques. Finally, we present a detailed analysis of the time-transition behavior of different motifs along with some of the real world highly productive motifs found in our dataset. This empirical study is a first step toward the development of a full-fledged recommendation system that can predict how productive a team would be in the future.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"2 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":"126388692","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: 3

Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection 基于冲突对约束的极值优化半监督社区检测算法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921580

Lei Li, Mei Du, Guanfeng Liu, Xuegang Hu, Gongqing Wu

{"title":"Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection","authors":"Lei Li, Mei Du, Guanfeng Liu, Xuegang Hu, Gongqing Wu","doi":"10.1109/ASONAM.2014.6921580","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921580","url":null,"abstract":"The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.","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":"115545130","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: 9

Extension of Modularity Density for overlapping community structure 重叠群落结构的模块化密度扩展

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921686

Mingming Chen, Konstantin Kuzmin, B. Szymanski

引用次数: 28

MOSAIC: Criminal network analysis for multi-modal surveillance and decision support 多模式监视和决策支持的犯罪网络分析

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921593

P. Seidler, R. Adderley, A. Badii, Matteo Raffaelli

引用次数: 2

Using single source data to better understand User-generated Content (UGC) behavior 使用单一来源数据来更好地理解用户生成内容(UGC)行为

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921676

Heng Lu, Jonathan J. H. Zhu

{"title":"Using single source data to better understand User-generated Content (UGC) behavior","authors":"Heng Lu, Jonathan J. H. Zhu","doi":"10.1109/ASONAM.2014.6921676","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921676","url":null,"abstract":"Single source refers to the unified measurement of different aspects of the same individual based on data from multiple sources. In the context of UGC, single source data can be used to study at least two important but as yet insufficiently investigated theoretical issues. First, single source data are ideal sources for studying inter-platform dynamics such as user migration across UGC platforms. Second, single source data can help to link individual self-reported cognitive factors with web crawled individual behavior logs, to achieve better understanding of individual behavior. In this paper, we select a random sample of Sina Blog users and collect their behavior information on both Sina Blog and Sina Weibo platforms; we also conduct an online survey to collect information about their cognitive factors. Merging all data together, we observe and quantify different behavior patterns of the same people across Blog and Weibo; we also identify alternative attractiveness and perceived popularity as significant drivers of one of the most important inter-platform dynamics - switching behavior.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"210 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":"114328790","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

Towards online anti-opinion spam: Spotting fake reviews from the review sequence 针对在线反意见垃圾:从评论序列中发现虚假评论

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921594

Yuming Lin, Tao Zhu, Hao Wu, Jingwei Zhang, Xiaoling Wang, Aoying Zhou

引用次数: 73

Adjustments to propensity score matching for network structures 网络结构倾向得分匹配的调整

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921651

Masoud Charkhabi

{"title":"Adjustments to propensity score matching for network structures","authors":"Masoud Charkhabi","doi":"10.1109/ASONAM.2014.6921651","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921651","url":null,"abstract":"Causal inference from observational data rely on similar treatment and control groups to isolate for variation, in addition to adjustments in estimates to account for the remaining uncontrollable variation. Propensity score matching and statistical inference are established tools to achieve for these two requirements respectively. Network structures in the underlying data of the experiment challenge this convention since they question assumptions of independent observations and increase the risk of unobserved variables. In this paper we approach the problem with the intent of preserving propensity score matching and inference, while accommodating network information. Multiple experiments are re-evaluated with network information. All experiments were intended to create organic growth through referrals in a financial services business. We offer first, the Propensity Score Layout; a rapid visualization approach to scan data from multiple studies that potentially may require re-evaluation due to network structure. Second, the Propensity Score Network Risk; a metric that captures the extent to which network structure interferes with the treatment of the experiment. And third; variables constructed from network information that to our surprise estimate the propensity score significantly better than node attributes. We also present a set of interesting problems for researchers in academia and industry. To the best of our knowledge network methods have not been studied thoroughly in this domain. We feel the combination of technique, results and domain are novel.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"7 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":"115352153","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: 3