2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第5页

A dynamic algorithm for local community detection in graphs 图中局部社区检测的动态算法

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809375

A. Zakrzewska, David A. Bader

引用次数: 54

Forecasting high tide: Predicting times of elevated activity in online social media 预测高潮:预测在线社交媒体活动增加的时间

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809392

Jimpei Harada, David M. Darmon, M. Girvan, W. Rand

{"title":"Forecasting high tide: Predicting times of elevated activity in online social media","authors":"Jimpei Harada, David M. Darmon, M. Girvan, W. Rand","doi":"10.1145/2808797.2809392","DOIUrl":"https://doi.org/10.1145/2808797.2809392","url":null,"abstract":"Social media provides a powerful platform for influencers to broadcast content to a large audience of followers. In order to reach the greatest number of users, an important first step is to identify times when a large portion of a target population is active on social media, which requires modeling the behavior of those individuals. We propose three methods for behavior modeling: a simple seasonality approach based on time-of-day and day-of-week, an autoregressive approach based on aggregate fluctuations from seasonality, and an aggregation-of-individuals approach based on modeling the behavior of individual users. We test these methods on data collected from a set of users on Twitter in 2011 and 2012. We find that the performance of the methods at predicting times of high activity depends strongly on the tradeoff between true and false positives, with no method dominating. Our results highlight the challenges and opportunities involved in modeling complex social systems, and demonstrate how influencers interested in forecasting potential user engagement can use complexity modeling to make better decisions.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134567506","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

Unsupervised graph-based patterns extraction for emotion classification 基于无监督图的情感分类模式提取

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809419

C. Argueta, Elvis Saravia, Yi-Shin Chen

引用次数: 11

Appropriateness of search engines, social networks, and directly approaching friends to satisfy information needs 适当的搜索引擎，社交网络，直接接近朋友，以满足信息需求

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808836

Christoph Fuchs, Georg Groh

引用次数: 2

A longitudinal study of the Google app market 对谷歌应用市场的纵向研究

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808823

Bogdan Carbunar, Rahul Potharaju

引用次数: 31

Hunting organization-targeted socialbots 寻找针对组织的社交机器人

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809396

Abigail Paradise, A. Shabtai, Rami Puzis

引用次数: 14

Fast community structure local uncovering by independent vertex-centred process 通过独立的顶点中心过程快速发现社区结构局部

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808866

M. Canu, Marcin Detyniecki, Marie-Jeanne Lesot, Adrien Revault d'Allonnes

引用次数: 4

Fine-grained geolocalisation of non-geotagged tweets 非地理标记推文的细粒度地理定位

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808869

P. Paraskevopoulos, Themis Palpanas

{"title":"Fine-grained geolocalisation of non-geotagged tweets","authors":"P. Paraskevopoulos, Themis Palpanas","doi":"10.1145/2808797.2808869","DOIUrl":"https://doi.org/10.1145/2808797.2808869","url":null,"abstract":"The rise in the use of social networks in the recent years has resulted in an abundance of information on different aspects of everyday social activities that is available online, with the most prominent and timely source of such information being Twitter. This has resulted in a proliferation of tools and applications that can help end-users and large-scale event organizers to better plan and manage their activities. In this process of analysis of the information originating from social networks, an important aspect is that of the geographic coordinates, i.e., geolocalisation, of the relevant information, which is necessary for several applications (e.g., on trending venues, traffic jams, etc.). Unfortunately, only a very small percentage of the Twitter posts are geotagged, which significantly restricts the applicability and utility of such applications. In this work, we address this problem by proposing a framework for geolocating tweets that are not geotagged. Our solution is general, and estimates the location from which a post was generated by exploiting the similarities in the content between this post and a set of geotagged tweets, as well as their time-evolution characteristics. Contrary to previous approaches, our framework aims at providing accurate geolocation estimates at fine grain (i.e., within a city). The experimental evaluation with real data demonstrates the efficiency and effectiveness of our approach.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123025567","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: 29

Mining open and crowdsourced data to improve situational awareness for railway 挖掘开放和众包数据，提高铁路态势感知能力

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809369

S. Rahman, J. Easton, C. Roberts

引用次数: 9

Significant edge detection in target network by exploring multiple auxiliary networks 通过探索多个辅助网络，对目标网络进行有效的边缘检测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809302

Nan Du, Jing Gao, Liang Ge, Vishrawas Gopalakrishnan, Xiaowei Jia, Kang Li, A. Zhang

{"title":"Significant edge detection in target network by exploring multiple auxiliary networks","authors":"Nan Du, Jing Gao, Liang Ge, Vishrawas Gopalakrishnan, Xiaowei Jia, Kang Li, A. Zhang","doi":"10.1145/2808797.2809302","DOIUrl":"https://doi.org/10.1145/2808797.2809302","url":null,"abstract":"Despite the ability to model many real world settings as a network, one major challenge in analyzing network data is that important and reliable links between objects are usually obscured by noisy information and hence not readily discernible. In this paper, we propose to detect these important and reliable links - significant edges, from a target network by using multiple auxiliary networks and a limited amount of labelled information. In this process, we first abstract the community knowledge learnt across target and auxiliary networks to detect significant patterns. The mined community knowledge captures the key profile of network relationships and thus can be used to determine whether an existing edge indicates a true or false relationship. Experiments on real world network data show that our two staged solution - a joint matrix factorisation procedure followed by edge significance score ranking, accurately predicts significant edges in target network by jointly exploring the underlying knowledge embedded in both target and auxiliary networks.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591043","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