2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第3页

A Syntax-based Learning Approach to Geo-locating Abnormal Traffic Events using Social Sensing 基于句法的学习方法在交通异常事件地理定位中的应用

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343708

Yang Zhang, Xiangyu Dong, D. Zhang, Dong Wang

{"title":"A Syntax-based Learning Approach to Geo-locating Abnormal Traffic Events using Social Sensing","authors":"Yang Zhang, Xiangyu Dong, D. Zhang, Dong Wang","doi":"10.1145/3341161.3343708","DOIUrl":"https://doi.org/10.1145/3341161.3343708","url":null,"abstract":"Social sensing has emerged as a new sensing paradigm to observe the physical world by exploring the “wisdom of crowd” on social media. This paper focuses on the abnormal traffic event localization problem using social media sensing. Two critical challenges exist in the state-of-the-arts: i) “content-only inference”: the limited and unstructured content of a social media post provides little clue to accurately infer the locations of the reported traffic events; ii) “informal and scarce data”: the language of the social media post (e.g., tweet) is informal and the number of the posts that report the abnormal traffic events is often quite small. To address the above challenges, we develop SyntaxLoc, a syntax-based probabilistic learning framework to accurately identify the location entities by exploring the syntax of social media content. We perform extensive experiments to evaluate the SyntaxLoc framework through real world case studies in both New York City and Los Angeles. Evaluation results demonstrate significant performance gains of the SyntaxLoc framework over state-of-the-art baselines in terms of accurately identifying the location entities that can be directly used to locate the abnormal traffic events.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125204306","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: 7

Content-Based Echo Chamber Detection on Social Media Platforms 基于内容的社交媒体平台回声室检测

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343689

Fernando H. Calderon, Li-Kai Cheng, Ming-Jen Lin, Yen-Hao Huang, Yi-Shin Chen

引用次数: 6

Increasing the Diffusional Characteristics of Networks Through Optimal Topology Changes within Sub-graphs 通过子图内最优拓扑变化提高网络的扩散特性

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3344823

Patryk Pazura, Jarosław Jankowski, Kamil Bortko, Piotr Bartków

{"title":"Increasing the Diffusional Characteristics of Networks Through Optimal Topology Changes within Sub-graphs","authors":"Patryk Pazura, Jarosław Jankowski, Kamil Bortko, Piotr Bartków","doi":"10.1145/3341161.3344823","DOIUrl":"https://doi.org/10.1145/3341161.3344823","url":null,"abstract":"In recent years, bustling online communities have focused a lot of attention on research dealing with information spreading. Through acquired knowledge about the characteristics of information spreading processes, we are able to influence their dynamics via the enhancement of propagation properties or by changing them to decrease their spread within a network. One of approaches is adding or removing connections within a network. While optimal linking within complex networks requires extensive computational resources, in this investigation, we focus on the optimization of the topology of small graphs within larger network structures. The study shows how the enhancement of propagation properties within small networks is preserved in bigger networks based on connected smaller graphs. We compare the results from combined small graphs with added links providing optimal spread and networks with additional random linking. The results show that improvements in linking within small sub-graphs with optimal linking improves the diffusional properties of the whole network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134145873","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: 1

The curse of self-presentation: Looking for career patterns in online CVs 自我展示的诅咒:在网上简历中寻找职业模式

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343681

Johanna M. Werz, Valerie Varney, I. Isenhardt

{"title":"The curse of self-presentation: Looking for career patterns in online CVs","authors":"Johanna M. Werz, Valerie Varney, I. Isenhardt","doi":"10.1145/3341161.3343681","DOIUrl":"https://doi.org/10.1145/3341161.3343681","url":null,"abstract":"Climbing the career ladder to a senior executive position is a long and complex process that, nevertheless, many people are trying to master. Over the last decades, the number of people providing their CVs on professional online social networks, such as LinkedIn is growing. New methods of pattern detection raise the question of whether online CVs provide insights into career patterns and paths. The respective hypothesis is that online CVs map people“s careers and therefore build the ideal data set to detect career patterns. To test this hypothesis, 100.006 online CVs were downloaded and preprocessed. This paper presents initial results of one educational and one internship variable. Whereas a higher degree positively predicts career level, having made an internship negatively relates to career level. These results reveal that rather than objectively mirroring people“s career trajectories, online career platforms provide selective information. The information of online CVs and the respective career level is intermingled, i.e. people with a high career level present different parts of their careers than people on lower levels. Furthermore, self-presentational effects might have an impact. The effect on similar research and possible implications are discussed.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190356","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

Daily life patients Sentiment Analysis model based on well-encoded embedding vocabulary for related-medication text 基于良好编码的相关药物文本嵌入词汇的日常生活患者情绪分析模型

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343854

Hanane Grissette, E. Nfaoui

{"title":"Daily life patients Sentiment Analysis model based on well-encoded embedding vocabulary for related-medication text","authors":"Hanane Grissette, E. Nfaoui","doi":"10.1145/3341161.3343854","DOIUrl":"https://doi.org/10.1145/3341161.3343854","url":null,"abstract":"Millions of health-related messages and fresh communications can reveal important public health issues. New Drugs, Diseases, Adverse Drug Reactions (ADRs) keep appearing on social media in new Unicode versions. In particular, generative Model for both Sentiment analysis (SA) and Naturel Language Understanding (NLU) requires medical human labeled data or making use of resources for weak supervision that operates with the ignorance and the inability to define related-medication targets, and results in inaccurate sentiment prediction performance. The frequent use of informal medical language, nonstandard format and abbreviation forms, as well as typos in social media messages has to be taken into account. We probe the transition-based approach between patients language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology[21] to be formal input of our neural network model. At this end, we propose daily life patients Sentiment Analysis model based on hybrid embedding vocabulary for related-medication text under distributed dependency, and concepts translation methodology by incorporating medical knowledge from social media and real life medical science systems. The proposed neural network layers is shared between medical concept Normalization model and sentiment prediction model in order to understand and leverage related-sentiment information behind conceptualized features in Multiple context. The experiments were performed on various real world scenarios where limited resources in this case.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253962","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: 10

On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts 社会网络的结构性质及其测量校准的合成对应物

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343686

Marcell Nagy, Roland Molontay

引用次数: 1

The Evolution of Roles 角色的演变

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342889

Julian Müller, U. Brandes

引用次数: 2

Optimal Influence Strategies in an Oligopolistic Competition Network Environment 寡头竞争网络环境下的最优影响策略

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343691

Dionisios N. Sotiropoulos, Ifigeneia Georgoula, Christos Bilanakos

{"title":"Optimal Influence Strategies in an Oligopolistic Competition Network Environment","authors":"Dionisios N. Sotiropoulos, Ifigeneia Georgoula, Christos Bilanakos","doi":"10.1145/3341161.3343691","DOIUrl":"https://doi.org/10.1145/3341161.3343691","url":null,"abstract":"This paper presents a non-linear optimization methodology for determining the Nash Equilibrium (NE) solutions of a non-cooperative two-player game. Each player, in particular, is trying to maximize a rational profit function within a continuous action space. The game arises in the context of a duopolistic network environment where two identical rival firms are competing to maximize their influence over a single consumer. Specifically, we consider a weighted and strongly connected network which mediates the opinion formation processes concerning the perceived qualities of their products. Obtaining the NE solutions for such a game is an extremely difficult task which cannot be analytically addressed, even if additional simplifying assumptions are imposed on the exogenous parameters of the model. Our approach, obtains the required NE solutions by combining the Karush-Kuhn-Tucker (KKT) conditions associated with the original optimization tasks into a single-objective nonlinear maximization problem under nonlinear constrains. The resulting optimization problem is, ultimately, solved through the utilization of the Sequential Quadratic Programming (SQP) algorithm which constitutes a state-of-the-art method for nonlinear optimization problems. The validity of our work is justified through the conduction of a series of experiments in which we simulated the best response-based dynamical behaviour of the two agents in the network that make strategic decisions. Juxtaposing the intersection points of the acquired best response curves against the NE solutions obtained by the proposed nonlinear optimization methodology verifies that the corresponding solution points coincide.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743257","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

Competitive Opinion Maximization in Social Networks 社交网络中的竞争性意见最大化

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342899

Jianjun Luo, Xinyue Liu, Xiangnan Kong

{"title":"Competitive Opinion Maximization in Social Networks","authors":"Jianjun Luo, Xinyue Liu, Xiangnan Kong","doi":"10.1145/3341161.3342899","DOIUrl":"https://doi.org/10.1145/3341161.3342899","url":null,"abstract":"Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124848899","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

Joint Role and Community Detection in Networks via L2,1 Norm Regularized Nonnegative Matrix Tri-Factorization 基于L2,1范数正则化非负矩阵三因子分解的网络联合角色与社区检测

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342886

Yulong Pei, G. Fletcher, Mykola Pechenizkiy

{"title":"Joint Role and Community Detection in Networks via L2,1 Norm Regularized Nonnegative Matrix Tri-Factorization","authors":"Yulong Pei, G. Fletcher, Mykola Pechenizkiy","doi":"10.1145/3341161.3342886","DOIUrl":"https://doi.org/10.1145/3341161.3342886","url":null,"abstract":"Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766532","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