Social Media Mining: An IntroductionCambridge University Press, 28 Απρ 2014 - 320 σελίδες The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining. |
Περιεχόμενα
Introduction | 1 |
Applications | 3 |
Graph Essentials | 13 |
Network Measures | 51 |
Network Models | 80 |
Data Mining Essentials | 105 |
Community Analysis | 141 |
Information Diffusion in Social Media | 179 |
Influence and Homophily | 217 |
Recommendation in Social Media | 245 |
Behavior Analytics | 271 |
Notes | 295 |
| 315 | |
Άλλες εκδόσεις - Προβολή όλων
Social Media Mining: An Introduction Reza Zafarani,Mohammad Ali Abbasi,Huan Liu Περιορισμένη προεπισκόπηση - 2014 |
Συχνά εμφανιζόμενοι όροι και φράσεις
activated adjacency matrix analyze assume average path length Bibliographic Notes centroids chapter class attribute value clique clustering coefficient collaborative filtering collective behavior community detection algorithms complete graph compute connected components Consider data mining dataset decision tree defined degree centrality degree distribution denote diffusion directed graph discussed distance eigenvalue eigenvector centrality Equation evaluate example Facebook flow friendships graph G(V homophily individuals instance interactions k-means labels maximum measure methods modularity neighbors node degrees node v₁ nodes normalized number of edges number of nodes observed PageRank pair of nodes power-law degree distribution predict preferential attachment preferential attachment model probability problem random graph ratings real-world networks recommendation regression represent sample selected set of nodes shortest path shown in Figure small-world model social media data social media mining social network spanning tree spectral clustering subgraphs supervised learning techniques Twitter undirected users vector weighted graph