2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)最新文献_第3页

An ant colony optimization method to detect communities in social networks 一种基于蚁群优化的社交网络社区检测方法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921583

S. Javadi, Shahram Khadivi, M. Shiri, Jia Xu

引用次数: 9

Populating knowledge base with collective entity mentions: A graph-based approach 用集体实体提及填充知识库:基于图的方法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921648

Hailun Lin, Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xiaojing Li, Xueqi Cheng

{"title":"Populating knowledge base with collective entity mentions: A graph-based approach","authors":"Hailun Lin, Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xiaojing Li, Xueqi Cheng","doi":"10.1109/ASONAM.2014.6921648","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921648","url":null,"abstract":"Populating a knowledge base with new entity mentions extracted from unstructured text can help enhance its coverage and freshness. It naturally consists of two subtasks, namely, fine-grained entity classification and entity linking. Existing studies often focus on one of these two subtasks and they usually populate entity mentions in the same text by implicitly assuming that they are independent. However, these entity mentions are often semantically related to each other and it would be better to populate them into the knowledge base collectively. For solving these problems, in this paper we propose an interdependence graph based and unified collective inference approach, called CIIGA, to populating a knowledge base with collective entities, which can jointly determine the proper locations of all entity mentions in the same text by exploiting their interdependence relationships. Experimental results show that this approach can achieve significant accuracy improvement, as compared to the baseline approach, APOLLO, on the task of knowledge base population with multiple entities.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"335 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":"116125649","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

Community discovery using social links and author-based sentiment topics 使用社会链接和基于作者的情感主题进行社区发现

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921645

Baoguo Yang, S. Manandhar

引用次数: 11

Topic dynamics in Weibo: Happy Entertainment dominates but angry Finance is more periodic 微博话题动态:快乐娱乐主导，愤怒财经周期性更强

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921588

Rui Fan, Jichang Zhao, X. Feng, Ke Xu

{"title":"Topic dynamics in Weibo: Happy Entertainment dominates but angry Finance is more periodic","authors":"Rui Fan, Jichang Zhao, X. Feng, Ke Xu","doi":"10.1109/ASONAM.2014.6921588","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921588","url":null,"abstract":"The tremendous development of online social media have changed people's life fundamentally in recent years. Weibo, a Twitter-like service in China, has attracted more than 500 million users in less than four years and produces more than 100 million Chinese tweets every day. In these massive tweets, different user interests and daily trends are reflected by different topics. While to our best knowledge, a systematic investigation of topic dynamics in Weibo is still missing. Aiming at filling this vital gap, we try to disclose the evolving patterns of topics from the perspective of time, geography, gender, emotion and interaction. First, an incremental learning framework is established to classify more than 200 million tweets into seven topics fast and accurately, whose F-measure arrives as high as 84%. Second, many interesting patterns in topic dynamics are revealed. For instance, happy Entertainment accounts for over half of the tweets and angry Finance possesses the most significant periodic pattern. Besides, the female and male users prefer different topics and Finance shows a surprisingly high correlation between connected users. Finally, our findings could provide insights for the topic-related applications in social media, like event detection or content recommendation.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"23 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":"114181637","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

A semantic model for academic social network analysis 学术社会网络分析的语义模型

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921602

Jie Hu, Mengchi Liu, Junchi Zhang

引用次数: 9

Finding social interaction patterns using call and proximity logs simultaneously 同时使用呼叫和接近记录查找社会互动模式

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921617

Yong-Jin Han, S. Cheng, Se-Young Park, Seong-Bae Park

引用次数: 3

A study of age and gender seen through mobile phone usage patterns in Mexico 通过墨西哥手机使用模式观察年龄和性别的研究

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921683

Carlos Sarraute, P. Blanc, Javier Burroni

引用次数: 55

Multi-label collective classification in multi-attribute multi-relational network data 多属性多关系网络数据的多标签集体分类

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921634

Priyesh Vijayan, Shivashankar Subramanian, Balaraman Ravindran

{"title":"Multi-label collective classification in multi-attribute multi-relational network data","authors":"Priyesh Vijayan, Shivashankar Subramanian, Balaraman Ravindran","doi":"10.1109/ASONAM.2014.6921634","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921634","url":null,"abstract":"Classical machine learning techniques assume the data to be i.i.d., but the real world data is inherently relational and can generally be represented using graphs or some variants of a graph representation. The importance of modeling relational data is evident from its increasing presence in many domains: Telecom networks, WWW, social networks, organizational networks, images, protein sequences, etc. This field has recently been receiving a lot of attention in various communities under different themes depending on the problem addressed and the nature of solution proposed. Collective classification is one such popular approach which involves the use of a local classifier that embeds the node's own attributes and neighbors' information in a feature vector, and classifies the nodes in an iterative procedure. Despite the increasing popularity, there is not much attention paid towards datasets with multiple attributes and multi-relational (MAMR) networks under multi-label scenarios. In MAMR data, nodes can be represented using multiple types of attributes (attribute views) and there are multiple link types between the nodes. For example, in Twitter, users can be represented using their tweets, urls shared, hashtags and list memberships. And different Twitter users can be connected using follower, followed by and re-tweet links. Secondly, in many networks, nodes are associated with more than one label. For instance, Twitter users can be tagged with one or more labels from a set L, where L contains various movie genres that a user might like. Motivated by this, we propose a learning technique for multi-label collective classification using multiple attribute views on multi-relational network data which captures complex label correlations within and across attribute/relationship types. We empirically evaluate our proposed approach on Twitter and MovieLens datasets, and we show that it performs better than the state-of-art approaches.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"21 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":"123732128","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

Bio-inspired models for characterizing YouTube viewcout 以生物为灵感的模型来描述YouTube的浏览量

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921600

Cédric Richier, E. Altman, R. E. Azouzi, T. Jiménez, G. Linarès, Y. Portilla

引用次数: 27

A novel method for computer video leaking signal detection 一种计算机视频泄漏信号检测的新方法

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921695

Jun Shi, Wei-qing Huang, Dong Wei, Degang Sun

引用次数: 3