Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第6页

ClassStrength ClassStrength

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110162

Walid Magdy, M. Eldesouky

引用次数: 3

Ego-centered community detection in directed and weighted networks 有向和加权网络中以自我为中心的社区检测

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3121243

A. O. E. Moctar, Idrissa Sarr

{"title":"Ego-centered community detection in directed and weighted networks","authors":"A. O. E. Moctar, Idrissa Sarr","doi":"10.1145/3110025.3121243","DOIUrl":"https://doi.org/10.1145/3110025.3121243","url":null,"abstract":"Community detection is one of the most studied topics in Social Network Analysis. Research in this realm has predominantly focus on finding out communities by considering the network as a whole. That is, all nodes are put in the same pool to define central metrics for finding out communities while ignoring the particularity of some nodes and their impact. Yet, if the position of some nodes matters when defining the metrics (i.e. node centric approach), the found communities may differ and can make more sens in real life situations. For instance, identifying the communities based on drug dealers and their interactions with others sounds better than finding communities while ignoring the individuals status. The purpose of this paper is to detect ego-centered community, which is defined as a community built from a particular node. Our solution is set to combine both link direction and weight, and therefore, differs from many existing solutions. Basically, we rely on a metric called a quality function that uses link properties to assess the cohesion of identified groups. Our method detect communities that reflect not only the structure but the reality regarding to the interaction nature in terms of intensity. We implement our solution and use \"Les Miserables\" dataset to demonstrate the effectiveness of our solution.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"88 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128828300","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: 6

HyperHeadTail: a Streaming Algorithm for Estimating the Degree Distribution of Dynamic Multigraphs HyperHeadTail:一种估计动态多图度分布的流算法

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3119395

Andrew Stolman, Kevin Matulef

引用次数: 3

Observe Locally Rank Globally 观察本地全球排名

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110063

A. Saxena, Ralucca Gera, S. Iyengar

引用次数: 9

Towards Diversified Local Users Identification Using Location Based Social Networks 利用基于位置的社交网络实现多样化的本地用户识别

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110159

Chao Huang, Dong Wang, Shenglong Zhu

{"title":"Towards Diversified Local Users Identification Using Location Based Social Networks","authors":"Chao Huang, Dong Wang, Shenglong Zhu","doi":"10.1145/3110025.3110159","DOIUrl":"https://doi.org/10.1145/3110025.3110159","url":null,"abstract":"Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128033253","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

Social Network Based Anomaly Detection of Organizational Behavior using Temporal Pattern Mining 基于时间模式挖掘的社会网络组织行为异常检测

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116200

Ze Li, Duoyong Sun, Feng Xu, Bo Li

{"title":"Social Network Based Anomaly Detection of Organizational Behavior using Temporal Pattern Mining","authors":"Ze Li, Duoyong Sun, Feng Xu, Bo Li","doi":"10.1145/3110025.3116200","DOIUrl":"https://doi.org/10.1145/3110025.3116200","url":null,"abstract":"The interaction between members within an organization evolves continuously and drives the organizational behavior to change over time. To understand the temporal behaviors of the organization and recognize the evolving characteristics of the networks, we need effective tools that can capture evolution of the objects. In this paper, we propose a novel and important problem in evolution behavior discovery. Given a series of snapshots of a temporal dataset, which are modeled as evolving social networks, our goal is to find networks which evolve in a dramatically different way that deviate from temporal pattern norm. We define such objects as organizational behavior anomalies. It is a challenging problem as temporal patterns are hidden deeply in noisy evolving datasets and thus it is difficult to distinguish anomalous objects from normal ones. To this end, we propose a social network based temporal pattern mining framework to detect organizational behavior anomalies. Specifically, we first give the definition and a brief description of organizational behavior anomalies. Then, we extract a number of features in the temporal dataset for profiling organizational networks. Third, we use the temporal pattern mining framework to evaluate anomalies of networks deviating from the normal evolutionary patterns. Within the proposed framework, a two-step procedure that exploits the strengths of unsupervised anomaly detection and supervised classification is used. Experimental results on two real-world datasets show that the proposed framework is highly effective in discovering interesting organizational behavior anomalies.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128093560","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

Automatic Construction of an Emoji Sentiment Lexicon 表情符号情感词典的自动构建

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110139

Mayu Kimura, Marie Katsurai

{"title":"Automatic Construction of an Emoji Sentiment Lexicon","authors":"Mayu Kimura, Marie Katsurai","doi":"10.1145/3110025.3110139","DOIUrl":"https://doi.org/10.1145/3110025.3110139","url":null,"abstract":"Emojis have been frequently used to express users' sentiments, emotions, and feelings in text-based communication. To facilitate sentiment analysis of users' posts, an emoji sentiment lexicon with positive, neutral, and negative scores has been recently constructed using manually labeled tweets. However, the number of emojis listed in the lexicon is smaller than that of currently existing emojis, and expanding the lexicon manually requires time and effort to reconstruct the labeled dataset. This paper presents a simple and efficient method for automatically constructing an emoji sentiment lexicon with arbitrary sentiment categories. The proposed method extracts sentiment words from WordNet-Affect and calculates the cooccurrence frequency between the sentiment words and each emoji. Based on the ratio of the number of occurrences of each emoji among the sentiment categories, each emoji is assigned a multidimensional vector whose elements indicate the strength of the corresponding sentiment. In experiments conducted on a collection of tweets, we show a high correlation between the conventional lexicon and our lexicon for three sentiment categories. We also show the results for a new lexicon constructed with additional sentiment categories.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127297402","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: 34

EmotionSensing: Predicting Mobile User Emotion 情感感知:预测移动用户情感

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110127

M. Roshanaei, Richard O. Han, Shivakant Mishra

引用次数: 7

Network analysis of NIH grant critiques NIH拨款批评的网络分析

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116212

D. Malikireddy, M. Jens, Amarette Filut, Anupama Bhattacharya, Elizabeth L. Pier, You Geon Lee, M. Carnes, A. Kaatz

引用次数: 2

On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks 情绪效价转移对社交网络中信息传播的影响

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110031

Ema Kusen, Mark Strembeck, Giuseppe Cascavilla, M. Conti

引用次数: 22