High-Value User Identification Based on Topic Weight

Abstract

The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances. INDEX TERMS Social network, role identification, high-value user, topic weight.

Topic-based influential user detection: a survey

“…While very few approaches have used manually handcrafted keywords to define multiple topics, [14,131], various other techniques, such as topic modeling [7,9,13,20,23,28,37,39,47,60,64,67,82,84,96,98,104,108,109,111,115,117,117,121,125,130,132], machine learning [85,108], and platform structures [50,126] are used to infer multiple topics in the literature. The way content is organized on a platform can be considered as a representation of a topic based on platform structures; e.g., a board on the Pinterest platform discusses a single topic.…”

“…In the literature, different techniques, such as statistical measures [18,26,29,53,63,81,93,99,106,107], random-walk approaches [2, 7, 7, 14, 16, 23, 24, 37, 39, 65, 67-69, 75, 76, 84, 88, 96, 108, 111, 115, 124, 126, 128], propagation algorithms [28,85,108,115], machine learning [25,50,[129][130][131], topic modeling [9,13,20,64,82,98,104,109,117,121], similarity measures [47,117,125], and optimization algorithms [113] are used for topic-based influential user detection. The Pagerank algorithm, i.e.…”

“…Instead of computing the topic-level influential score for a user, Zhao et al [125] compute an overall influence score using collective credit allocation (CCA) approach [95]. The CCA approach was proposed to allocate credits for authors of a paper using the information available in the citation network.…”

“…Neural network [131] Topic modeling Random-walk approach LDA (T) & PageRank (I) [7,23,37,39,67,84,96,108,111] Propagation algorithms LDA (T) & Affinity propagation (I) [28] LDA (T) & Markov Model (I) [115] Machine learning LDA (T), Decision tree (I), Random forest (I) & Gradient boosting (I) [25] seededLDA & Neural network (I) [130] Biterm topic model & Matrix factorization (I) [60] Similarity measures LDA (T) [47,125] Guided hierarchical LDA [117] Machine learning Probabilistic approach Weighted partition around Medoids (T) & Maximum likelihood estimation (I) [85] Naive Bayes classifier (T) & Singular value decomposition (I) [108] Platform structure…”

“…Therefore, it is essential to analyze the user influence at the topic level and determine the topic-based influential users. This can be considered as a granular study of influential user detection problem, and a vast amount of interest has been shown towards inferring topic-based influential users in the literature [13,26,37,38,64,69,78,98,111,125].…”