Exploiting homophily effect for trust prediction

By Jiliang Tang, Huiji Gao, Xia Hu, Huan Liu. Web Search and Data Mining, 2013

312 citations recorded by the source corpus.

How researchers see this paper

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Unsupervised sentiment analysis with emotional signals (2013, background): “It is also interesting to study other emotion correlation information, like spatial patterns and homophily effect [33], to measure the sentiment orientation of social media posts as well.”
Recommender Systems (2016, unknown): “The SoRec algorithm is proposed in [381], and the LOCALBAL algorithm in [594].”
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis (2014, background): “First, whereas they model person-to-item evaluations, we model person-to-person evaluations; these are also the focus of Tang et al. (2013), who, though, use an unsigned network, whereas our work is geared toward distinguishing positive and negative edge labels.”
Label Informed Attributed Network Embedding (2017, background): “In addition, a number of data mining applications, such as sentiment analysis [12] and trust prediction [34], have been benefited by exploiting the correlations between geometrical structure and node attributes.”
Accelerated Attributed Network Embedding (2017, background): “It becomes increasingly promising to advance the learning performance by jointly exploiting geometrical structures and node attributes [13, 21, 33].”