Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining最新文献

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Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3343698

Maria Serna, Subrata Acharya

引用次数: 0

gl2vec

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3342908

Kun Tu, Jian Li, D. Towsley, Dave Braines, Liam D. Turner

引用次数: 3

OPTANE

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3342937

Farzan Masrour, P. Tan, A. Esfahanian

引用次数: 1

RiskCast RiskCast

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3342912

Yang Zhang, Hongxiao Wang, D. Zhang, Yiwen Lu, Dong Wang

{"title":"RiskCast","authors":"Yang Zhang, Hongxiao Wang, D. Zhang, Yiwen Lu, Dong Wang","doi":"10.1145/3341161.3342912","DOIUrl":"https://doi.org/10.1145/3341161.3342912","url":null,"abstract":"Road traffic accidents are a major challenge in urban transportation systems. An effective countermeasure to address this problem is to accurately forecast the traffic risks in a city before accidents actually happen. Current traffic accident prediction solutions largely rely on accurate data collected from infrastructure-based sensors, which is not always available due to various resource constraints or privacy and legal concerns. In this paper, we address this limitation by exploring social sensing, a new sensing paradigm that uses humans as sensors to report the states of the physical world. In particular, we consider two types of publicly available social sensing data sources: social media data (e.g., traffic posts on Twitter) and open city data (e.g., traffic data from the city web portal). In this paper, we develop the RiskCast, an inductive multi-view learning approach to accurately forecast the traffic risk by exploiting the social sensing data under a principled co-regularization framework. The evaluation results on a real world dataset from New York City show that RiskCast significantly outperforms the state-of-the-art baselines in forecasting the traffic risks in a city.","PeriodicalId":229882,"journal":{"name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116120497","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

The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions 不相似的含义:对用于评估社区检测解决方案的各种相似性量化方法的评价

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3342941

Obaida Hanteer, L. Rossi

{"title":"The meaning of dissimilar: an evaluation of various similarity quantification approaches used to evaluate community detection solutions","authors":"Obaida Hanteer, L. Rossi","doi":"10.1145/3341161.3342941","DOIUrl":"https://doi.org/10.1145/3341161.3342941","url":null,"abstract":"Evaluating a community detection method involves measuring the extent to which the resulted solution, i.e clustering, is similar to an optimal solution, a ground truth. Different normalized similarity indices have been proposed in the literature to quantify the extent to which two clusterings are similar where 1 refers to a perfect agreement between them (i.e the two clusterings are identical) and 0 refers to a perfect disagreement. While interpreting the similarity score 1 seems to be intuitive, it does not seem to be so when the similarity score is otherwise suggesting a level of disagreement between the compared clusterings. That is because there is no universal definition of dissimilarity when it comes to comparing two clusterings. In this paper, we address this issue by first providing a taxonomy of similarity indices commonly used for evaluating community detection solutions. We then elaborate on the meaning of clusterings dissimilarity and the types of possible dissimilarities that can exist among two clusterings in the context of community detection. We perform an extensive evaluation to study the behaviour of different similarity indices as a function of the dissimilarity type with both disjoint and non-disjoint clusterings. We finally provide practitioners with some insights on which similarity indices to use for the task at hand and how to interpret their values.","PeriodicalId":229882,"journal":{"name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123082628","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

SAVIZ

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3343703

M. Kejriwal, Peilin Zhou

{"title":"SAVIZ","authors":"M. Kejriwal, Peilin Zhou","doi":"10.1145/3341161.3343703","DOIUrl":"https://doi.org/10.1145/3341161.3343703","url":null,"abstract":"Due to climate change and the effects of geopolitical and social challenges like the refugee crisis in Europe, the world is facing an unprecedented set of humanitarian problems. According to the United Nations, there is a projected funding shortfall of more than 20 billion dollars in addressing these needs. Technology can play a vital role in mitigating this burden, especially with the advent of real-time social media and advances in areas like Natural Language Processing and machine learning. An important problem addressed by machine learning in current crisis informatics platforms is situation labeling, which can be intuitively defined as semi-automatically assigning one or more actionable labels (such as food, medicine or water) to tweets or documents from a controlled vocabulary. Despite multiple advances, current situation labeling systems are noisy and do not generalize very well to arbitrary crisis data. Consequentially, consumers of these outputs (which include humanitarian responders) are unwilling to trust these outputs without due diligence or provenance. In this paper, we demonstrate an interactive visualization platform called SAVIZ that provides non-technical first responders with such capabilities. SAVIZ is completely built using open-source technologies, can be rendered on a web browser and is backward-compatible with several pre-existing crisis intelligence platforms. We use two real-world scenarios (the 2015 earthquake in Nepal, and the unfolding Ebola crisis in Africa) to illustrate the potential of SAVIZ.","PeriodicalId":229882,"journal":{"name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126783447","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

DeepGx DeepGx

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3343516

Joseph M. de Guia, M. Devaraj, C. Leung

{"title":"DeepGx","authors":"Joseph M. de Guia, M. Devaraj, C. Leung","doi":"10.1145/3341161.3343516","DOIUrl":"https://doi.org/10.1145/3341161.3343516","url":null,"abstract":"This paper aims to explore the problems associated in solving the classification of cancer in gene expression data using deep learning model. Our proposed solution for the cancer classification of ribonucleic acid sequencing (RNA-seq) extracted from the Pan-Cancer Atlas is to transform the 1-dimensional (1D) gene expression values into 2-dimensional (2D) images. This solution of embedding the gene expression values into a 2D image considers the overall features of the genes and computes features that are needed in the classification task of the deep learning model by using the convolutional neural network (CNN). When training and testing the 33 cohorts of cancer types in the convolutional neural network, our classification model led to an accuracy of 95.65%. This result is reasonably good when compared with existing works that use multiclass label classification. We also examine the genes based on their significance related to cancer types through the heat map and associate them with biomarkers. Our CNN for the classification task fosters the deep learning framework in the cancer genome analysis and leads to better understanding of complex features in cancer disease.","PeriodicalId":229882,"journal":{"name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115964436","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: 56

iTrustSO

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-08-27 DOI: 10.1145/3341161.3343524

Lingwei Chen, Shifu Hou, Yanfang Ye, T. Bourlai, Shouhuai Xu, Liang Zhao

{"title":"iTrustSO","authors":"Lingwei Chen, Shifu Hou, Yanfang Ye, T. Bourlai, Shouhuai Xu, Liang Zhao","doi":"10.1145/3341161.3343524","DOIUrl":"https://doi.org/10.1145/3341161.3343524","url":null,"abstract":"Despite the apparent benefits of modern social coding paradigm such as Stack Overflow, its potential security risks have been largely overlooked (e.g., insecure codes could be easily embedded and distributed). To address this imminent issue, in this paper, we bring a significant insight to leverage both social coding properties and code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers and code snippets in Stack Overflow. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use meta-path based approach to incorporate higher-level semantics to build up relatedness over code snippets. Later, we propose a novel hierarchical attention-based sequence learning model named CodeHin2Vec to seamlessly integrate node (i.e., code snippet) content with HIN-based relations for representation learning. After that, a classifier is built for insecure code snippet detection. Integrating our proposed method, an intelligent system named iTrustSO is accordingly developed to address the code security issues in modern software coding platforms. Comprehensive experiments on the data collections from Stack Overflow are conducted to validate the effectiveness of our developed system iTrustSO by comparisons with alternative methods.","PeriodicalId":229882,"journal":{"name":"Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240556","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