標題: Titlebook: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining; Nitin Agarwal,Nima Dokoohaki,Serpil To [打印本頁] 作者: 悲傷我 時間: 2025-3-21 17:48
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining影響因子(影響力)
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining影響因子(影響力)學科排名
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書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining網(wǎng)絡公開度學科排名
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining被引頻次
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining被引頻次學科排名
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining年度引用
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining年度引用學科排名
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining讀者反饋
書目名稱Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining讀者反饋學科排名
作者: CURB 時間: 2025-3-21 21:13 作者: 傳染 時間: 2025-3-22 01:51 作者: 一條卷發(fā) 時間: 2025-3-22 05:52
978-3-030-06797-7Springer International Publishing AG, part of Springer Nature 2019作者: finite 時間: 2025-3-22 11:09
Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining978-3-319-94105-9Series ISSN 2190-5428 Series E-ISSN 2190-5436 作者: 標準 時間: 2025-3-22 16:06
,Bluetooth auf den Zahn gefühlt,cial media platforms often offer help and emotional support to others (the good), they also spam (the bad) and harass others as well as even manipulate others via fake news (the ugly). In order to both leverage the positive effects and mitigate the negative effects of using social media, intent mini作者: 標準 時間: 2025-3-22 20:19
Network Infrastructure Securityial media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illu作者: Pandemic 時間: 2025-3-23 00:30 作者: UTTER 時間: 2025-3-23 04:50 作者: 抱怨 時間: 2025-3-23 07:56
Fundamentals of the J Programming Language, local topological features of a network. This is relevant, for example, when fitting a random graph model to a real-world network. With respect to existing measures the model might look like a good fit, however, the local topology might be very different. In the article we propose a new characteriz作者: Communal 時間: 2025-3-23 10:18
Deepak Kakadia,Jin Yang,Alexander Gilgurcontinuously increasing volume of data exchanged between those users, it is reasonable to think of methods to improve information accuracy and also protect users’ privacy. In this research we proposed a weighted-based approach to describe relations between users in OSNs. Users in OSNs interact with 作者: sacrum 時間: 2025-3-23 16:17 作者: leniency 時間: 2025-3-23 19:55 作者: 神刊 時間: 2025-3-23 23:16
Network Radar Countermeasure Systems,ccounts readily generate Big Data marked by velocity, volume, value, variety, and veracity challenges. This type of Big Data analytics already supports useful investigations ranging from research into data mining and developing public policy to actions targeting an individual in a variety of domains作者: 沒收 時間: 2025-3-24 03:00
Customer Relationship Management,ersification of platforms, from crowdsourcing ones, social computing platforms (in terms of collaborative task execution), and online labor/expert markets to collective adaptive systems (CAS) with humans-in-the-loop. Despite the advancements in various mechanisms to support effective provisioning of作者: Nebulous 時間: 2025-3-24 09:35 作者: 就職 時間: 2025-3-24 11:07
Privacy in Human Computation: User Awareness Study, Implications for Existing Platforms, Recommendatecting mechanisms, we conducted an online survey study to assess user privacy awareness in human computation systems and in this paper provide the results of it. Lastly, we provide recommendations for developers for designing privacy-preserving human computation platforms as well as research directions.作者: Cubicle 時間: 2025-3-24 17:48 作者: STEER 時間: 2025-3-24 19:48
Predictive Analysis on Twitter: Techniques and Applications, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.作者: aquatic 時間: 2025-3-25 03:07 作者: Throttle 時間: 2025-3-25 04:02 作者: 紳士 時間: 2025-3-25 08:45
Book 2019rstanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, be作者: LINE 時間: 2025-3-25 15:31 作者: 陪審團每個人 時間: 2025-3-25 16:38 作者: 不理會 時間: 2025-3-25 22:40
Studying Fake News via Network Analysis: Detection and Mitigation i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.作者: progestin 時間: 2025-3-26 00:45
Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Modelse for large networks we propose two new sampling schemes to approximate the distribution. Finally, we perform some experiments comparing a set of datasets with respect to their pattern distributions and comparing the fit of some random graph models to the datasets.作者: 大笑 時間: 2025-3-26 05:24 作者: hurricane 時間: 2025-3-26 10:37 作者: foreign 時間: 2025-3-26 13:50
Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analyticsial media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illu作者: eczema 時間: 2025-3-26 20:28
Studying Fake News via Network Analysis: Detection and Mitigationo enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Identifying and mitigating fake news also presents unique challenges. To tackle these challenges, many existing research efforts e作者: bile648 時間: 2025-3-26 21:04
Predictive Analysis on Twitter: Techniques and Applications essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques作者: 殺人 時間: 2025-3-27 03:29
Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models local topological features of a network. This is relevant, for example, when fitting a random graph model to a real-world network. With respect to existing measures the model might look like a good fit, however, the local topology might be very different. In the article we propose a new characteriz作者: 影響深遠 時間: 2025-3-27 05:36 作者: 疲憊的老馬 時間: 2025-3-27 10:30 作者: Provenance 時間: 2025-3-27 14:15 作者: ARK 時間: 2025-3-27 18:41
Domain-Specific Use Cases for Knowledge-Enabled Social Media Analysisccounts readily generate Big Data marked by velocity, volume, value, variety, and veracity challenges. This type of Big Data analytics already supports useful investigations ranging from research into data mining and developing public policy to actions targeting an individual in a variety of domains作者: 搬運工 時間: 2025-3-28 01:35 作者: 顛簸地移動 時間: 2025-3-28 03:45 作者: Harrowing 時間: 2025-3-28 06:39
Deepak Kakadia,Jin Yang,Alexander Gilgure is a group of users in which everyone is a friend to all other group members. Interactions between cliques’ members are studied in different networks for knowledge extraction. We introduced the concept of “weighted cliques” in comparison with classical cliques to provide better understanding of us作者: Hyaluronic-Acid 時間: 2025-3-28 13:49
Common Network Pharmacology Databases,the analysis of overlapping communities. Overlapping community structures are suitable indicators as for a real analysis in this domain. As such, we propose a two-phase algorithm based on two significant rather simple social dynamics named Disassortative degree Mixing and Information Diffusion—this 作者: 肥料 時間: 2025-3-28 16:50
Managing Character Sets and Encodings,orporates supervision through external labeling and capability of quickly digesting real-time updates thus making it more adaptive to Twitter and platforms alike. Our proposed extension has capability of handling large quantities of newly arrived documents in a stream, and at the same time, is capab作者: 或者發(fā)神韻 時間: 2025-3-28 19:34 作者: 宣稱 時間: 2025-3-28 23:00 作者: 比賽用背帶 時間: 2025-3-29 03:15 作者: defray 時間: 2025-3-29 09:22 作者: 尖牙 時間: 2025-3-29 12:58
