2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第10页

Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning 利用深度学习融合气象数据、卫星图像和社交媒体图像的见解，预测临近空气质量

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508698

Muhammad Rizal Khaefi, Zakiya Pramestri, Imaduddin Amin, Jong Gun Lee

{"title":"Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning","authors":"Muhammad Rizal Khaefi, Zakiya Pramestri, Imaduddin Amin, Jong Gun Lee","doi":"10.1109/ASONAM.2018.8508698","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508698","url":null,"abstract":"Peatland fire and haze events in Southeast Asia are disasters with trans-boundary implications, having increased in recent years along with rapid deforestation, land clearing and severe dry seasons. Aerosols are emitted in high concentrations from the fires, which degrade air quality and reduce visibility, in turn causing economic, social, health, and environmental problems. During haze events, it is critical for public authorities to have timely information about affected populations. Currently, Indonesian disaster management authorities manage forest and peatland fire and haze events based on satellite data and sensors. They are looking for more real-time information in order to better protect vulnerable populations and environment. This paper explores information on visibility extracted from photos shared on social media to improve forecasting performance for haze severity. Our results show that visibility information can improve forecast accuracy over a baseline approach with common features, namely data from satellites and ground air quality sensors. Furthermore, by using social media photos, our model adds a near real-time property to the forecast model, with potential to improve disaster management and mitigation,","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122679925","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

Community Detection with Edge Augmentation in Criminal Networks 基于边缘增强的犯罪网络社区检测

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508326

Ashwin Bahulkar, B. Szymanski, N. O. Baycik, Thomas C. Sharkey

引用次数: 30

Meta-Terrorism: Identifying Linguistic Patterns in Public Discourse After an Attack 元恐怖主义:攻击后公共话语的语言模式识别

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508647

Panos Kostakos, Markus Nykanen, Mikael Martinviita, Abhinay Pandya, M. Oussalah

引用次数: 10

Transformation and Commonality of Spatial Organization of Christian Church by Social Network Analysis 社会网络分析下基督教教堂空间组织的变迁与共性

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508300

Yi-Chun Huang, Y. Chiou

引用次数: 1

Outfit Recommender System 服装推荐系统

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508656

Nikita Ramesh, Teng-Sheng Moh

{"title":"Outfit Recommender System","authors":"Nikita Ramesh, Teng-Sheng Moh","doi":"10.1109/ASONAM.2018.8508656","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508656","url":null,"abstract":"The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommender systems on retail websites generate a lot of this revenue. Thus, improving recommender systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommender system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131143965","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: 8

Wisdom in Adversity: A Twitter Study of the Japanese Tsunami 逆境中的智慧:日本海啸的推特研究

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508253

Paolo Casani, Hayate Iso, Shoko Wakamiya, E. Aramaki

引用次数: 0

Exploiting User Actions for App Recommendations 利用用户行为进行应用推荐

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508447

Kai Shu, Suhang Wang, Huan Liu, Jiliang Tang, Yi Chang, Ping Luo

{"title":"Exploiting User Actions for App Recommendations","authors":"Kai Shu, Suhang Wang, Huan Liu, Jiliang Tang, Yi Chang, Ping Luo","doi":"10.1109/ASONAM.2018.8508447","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508447","url":null,"abstract":"Mobile Applications (or Apps) are becoming more and more popular in recent years, which has attracted increasing attention on mobile App recommendations. The majority of existing App recommendation algorithms focus on mining App functionality or user usage data for discovering user preferences; while actions taken by a user when he/she decides to download an App or not are ignored. In realistic scenarios, a user will first view the description of the App and then decide if he/she wants to download it or not. The actions such as viewing or downloading provide rich information about users' preferences and tastes for Apps, which have great potentials to advance App recommendations. However, the work on exploring action data for App recommendations is rather limited. Therefore, in this paper we study the novel problem of exploiting user actions for App recommendations. We propose a new framework ActionRank, which simultaneously captures various signals from user actions for App recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919621","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

The 4th International Workshop on Dynamics on and of Networks (DYNO 2018) 第四届网络动力学国际研讨会(DYNO 2018)

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/asonam.2018.8508358

Giulio Rossetti, N. Ilhan, R. Pensa, Sho Tsugawa

引用次数: 0

A Nonnegative Matrix Factorization Approach for Multiple Local Community Detection 多局部社团检测的非负矩阵分解方法

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508796

Dany Kamuhanda, Kun He

{"title":"A Nonnegative Matrix Factorization Approach for Multiple Local Community Detection","authors":"Dany Kamuhanda, Kun He","doi":"10.1109/ASONAM.2018.8508796","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508796","url":null,"abstract":"Existing works on local community detection in social networks focus on finding one single community a few seed members are most likely to be in. In this work, we address a much harder problem of multiple local community detection and propose a Nonnegative Matrix Factorization algorithm for finding multiple local communities for a single seed chosen randomly in multiple ground truth communities. The number of detected communities for the seed is determined automatically by the algorithm. We first apply a Breadth-First Search to sample the input graph up to several levels depending on the network density. We then use Nonnegative Matrix Factorization on the adjacency matrix of the sampled subgraph to estimate the number of communities, and then cluster the nodes of the subgraph into communities. Our proposed method differs from the existing NMF-based community detection methods as it does not use“ argmax ” function to assign nodes to communities. Our method has been evaluated on real-world networks and shows good accuracy as evaluated by the F1 score when comparing with the state-of-the-art local community detection algorithm.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115909605","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: 15

Identifying Influential Nodes to Inhibit Bootstrap Percolation on Hyperbolic Networks 识别双曲网络上抑制自举渗透的影响节点

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508248

Christine Marshall, J. Cruickshank, C. O'Riordan

{"title":"Identifying Influential Nodes to Inhibit Bootstrap Percolation on Hyperbolic Networks","authors":"Christine Marshall, J. Cruickshank, C. O'Riordan","doi":"10.1109/ASONAM.2018.8508248","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508248","url":null,"abstract":"This work involves agent-based simulation of bootstrap percolation on hyperbolic networks. Our goal is to identify influential nodes in a network which might inhibit the percolation process. Our motivation, given a small scale random seeding of an activity in a network, is to identify the most influential nodes in a network to inhibit the spread of an activity amongst the general population of agents. This might model obstructing the spread of fake news in an on line social network, or cascades of panic selling in a network of mutual funds, based on rumour propagation. Hyperbolic networks typically display power law degree distribution, high clustering and skewed centrality distributions. We introduce a form of immunity into the networks, targeting nodes of high centrality and low clustering to be immune to the percolation process, then comparing outcomes with standard bootstrap percolation and with random selection of immune nodes. We generally observe that targeting nodes of high degree has a delaying effect on percolation but, for our chosen graph centralisation measures, a high degree of skew in the distribution of local node centrality values bears some correlation with an increased inhibitory imnact on percolation.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123996407","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