Proceedings of the 7th ACM IKDD CoDS and 25th COMAD最新文献

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Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371220

N. Harilal, Rushil Shah, Saumitra Sharma, Vedanta Bhutani

引用次数: 2

Ranking Marginal Influencers in a Target-labeled Network 目标标记网络中边际影响者的排名

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371197

P. Lohia, Kalapriya Kannan, Karan Rai, Srikanta J. Bedathur

{"title":"Ranking Marginal Influencers in a Target-labeled Network","authors":"P. Lohia, Kalapriya Kannan, Karan Rai, Srikanta J. Bedathur","doi":"10.1145/3371158.3371197","DOIUrl":"https://doi.org/10.1145/3371158.3371197","url":null,"abstract":"Using social networks for spreading marketing information is a commonly used strategy to help in quick adoption of innovations, retention of customers and for improving brand awareness. In many settings, the set of entities in the network who must be the targets of such an information spread are already known, either implicitly or explicitly. It would still be beneficial to route the information to them through a carefully chosen set of influencers in the network. We term networks where we have such vertices labeled as targeted recipients as targeted networks. For instance, in an online marketing channel of a fashion product, where vertices are tagged with 'fashion' as their preferred choice of online shopping, forms a targeted network. In such targeted networks, how to select a small subset of vertices that maximizes the influence over target nodes while simultaneously minimizing the non-target nodes which get the information (e.g., to reduce their spam, or in some cases, due to costs)? We term this as the problem of maximizing the marginal influence over target networks and propose an iterative algorithm to solve this problem. We present the results of our experiment with large information networks, derived from English Wikipedia graph, which show that the proposed algorithm effectively identifies influential nodes that help reach pages identified through queries/topics. Qualitative analysis of our results shows that we can generate a semantically meaningful ranking of query-specific influential nodes.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066976","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

Personalized Ranking in Collaborative Filtering: Exploiting l-th Order Transitive Relations of Social Ties 协同过滤中的个性化排序:利用社会关系的l阶传递关系

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371189

Bithika Pal, M. Jenamani

引用次数: 0

Temporal Prediction of Socio-economic Indicators Using Satellite Imagery 利用卫星图像进行社会经济指标的时间预测

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371167

Chahat Bansal, Arpita Jain, Phaneesh Barwaria, Anuj Choudhary, Anupam Singh, Ayush Gupta, Aaditeshwar Seth

{"title":"Temporal Prediction of Socio-economic Indicators Using Satellite Imagery","authors":"Chahat Bansal, Arpita Jain, Phaneesh Barwaria, Anuj Choudhary, Anupam Singh, Ayush Gupta, Aaditeshwar Seth","doi":"10.1145/3371158.3371167","DOIUrl":"https://doi.org/10.1145/3371158.3371167","url":null,"abstract":"Machine learning models based on satellite data have been actively researched to serve as a proxy for the prediction of socio-economic development indicators. Such models have however rarely been tested for transferability over time, i.e. whether models learned on data for a certain year are able to make accurate predictions on data for another year. Using a dataset from the Indian census at two time points, for the years 2001 and 2011, we evaluate the temporal transferability of a simple machine learning model at sub-national scales of districts and propose a generic method to improve its performance. This method can be especially relevant when training datasets are small to train a robust prediction model. Then, we go further to build an aggregate development index at the district-level, on the lines of the Human Development Index (HDI) and demonstrate high accuracy in predicting the index based on satellite data for different years. This can be used to build applications to guide data-driven policy making at fine spatial and temporal scales, without the need to conduct frequent expensive censuses and surveys on the ground.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567175","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: 12

Describing Patterns of Socio-Economic Development at Fine Spatial and Temporal Resolutions 以精细的时空分辨率描述社会经济发展模式

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371214

Chahat Bansal

引用次数: 2

Understanding the Political Inclination of WhatsApp Chats 了解WhatsApp聊天的政治倾向

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371227

Anshuman Yadav, Aditya Garg, Anup Aglawe, Ayush Agarwal, Vivek Srivastava

引用次数: 4

Improving Convergence in IRGAN with PPO 提高IRGAN与PPO的收敛性

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371209

Moksh Jain, S. Kamath

引用次数: 2

Coverage Pattern Mining Based on MapReduce 基于MapReduce的覆盖模式挖掘

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371188

Akhil Ralla, Shadaab Siddiqie, P. K. Reddy, Anirban Mondal

引用次数: 5

Prototype Selection and Dimensionality Reduction on Multi-Label Data 多标签数据的原型选择与降维

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371184

Hemavati, V. Devi, Seba Ann Kuruvilla, R. Aparna

引用次数: 3

Computational Fact Validation from Knowledge Graph using Structured and Unstructured Information 利用结构化和非结构化信息从知识图谱中进行事实计算验证

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Pub Date : 2020-01-05 DOI: 10.1145/3371158.3371187

Saransh Khandelwal, D. Kumar

{"title":"Computational Fact Validation from Knowledge Graph using Structured and Unstructured Information","authors":"Saransh Khandelwal, D. Kumar","doi":"10.1145/3371158.3371187","DOIUrl":"https://doi.org/10.1145/3371158.3371187","url":null,"abstract":"In today's world, data or information is increasing at an exponential rate, and so is the fake news. Traditional fact-checking methods like fake news detection by experts, analysts, or some organizations do not match with the volume of information available. This is where the problem of computational fact-checking or validation becomes relevant. Given a Knowledge Graph, a knowledge corpus, and a fact (triple statement), the goal of fact-checking is to decide whether the fact or knowledge is correct or not. Existing approaches extensively used several structural features of the input Knowledge Graph to address the mentioned problem. In this work, our primary focus would be to leverage the unstructured information along with the structured ones. Our approach considers finding evidence from Wikipedia and structured information from Wikidata, which helps in determining the validity of the input facts. As features from the structured domain, we have used TransE embedding considering components of the input fact. The similarity of input fact with elements of relevant Wikipedia pages has been used as unstructured features. The experiments with a dataset consisting of nine relations of Wikidata has established the advantage of combining unstructured features with structured features for the given task.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533963","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