CCF B类期刊IPM专刊截稿信息1条
数据库管理与信息检索
Information Processing & Management
Special Issue on Mining Social Influence and Actionable Insights from Social Networks
全文截稿: 2018-10-15
影响因子: 3.444
CCF分类: B类
中科院JCR分区:
• 大类 : 工程技术 - 3区
• 小类 : 计算机：信息系统 - 3区
网址: http://www.journals.elsevier.com/information-processing-and-management/
We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes:
Predictive modeling based on social networks such as
o Box office prediction
o Election prediction
o Flu prediction
Product adaptation models with social networks such as
o Sale price prediction
o New product popularity prediction
o Brand popularity
o Business downfall prediction
User modeling and social networks including
o Predict users daily activities including recurring events
o User churn prediction
o Determining user similarities, trustworthiness and reliability
Social networks and information/knowledge dissemination
o Topic and trend prediction
o Prediction of information diffusion patterns
o Identification of causality and correlation between event/topics/communities
Social network analysis and measures
o Network topology
o Centrality measures
o Community detection
o Dynamic network models
o Diffusion models
Information diffusion modeling with social networks
o Information propagation and assimilation in social networks
o Sentiment diffusion in social networks
o Competitive intelligence from social networks
Social influence analysis on online social networks
o Systems and algorithms for discovering influential users
o Recommending influential users in online social networks
o Social influence maximization
o Modeling social networks and behavior for discovering influential users
o Discovering influencers for advertising and viral marketing in social networks
o Decision support systems and influencer discovering
Trust and reputation in social networks
Merging internal (proprietary) data with social data
Feature Engineering from Social Networks
Datasets and Evaluation methodologies for predictive modeling in social networks
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信息处理和管理（IPM）在计算机与信息科学的交叉点上发布了有关领域，包括但不限于商业、市场营销、广告、社交计算和信息技术等领域的理论、方法或应用的前沿研究。该杂志的目的是通过为及时传播高级和热门问题提供有效的论坛，从而在计算机与信息科学的交叉点上增进研究人员和从业人员的利益。该期刊对原始研究文章、研究调查文章、研究方法文章以及涉及研究关键应用的文章特别感兴趣。官网地址：http://dblp.uni-trier.de/db/journals/ipm/
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