DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table

A Survey of Social Network - Word Embedding Approach for Hate Speeches Detection

Word embedding is a technique to represent sentences in vector space. The representation itself is carried-out to build a model that would suffice in representing a particular task related to the use of the sentence itself, for example, a model of similarity among sentences/words, a model of Twitter user connectivity, and demographics of tweets model. The use of word embedding is a handful to the sentiment analysis research because it helps build a mathematical-friendly model from sentences. The model then will be suitable as feeds for the other computational process.Word embedding is a technique to represent sentences in vector space. The representation itself is carried-out to build a model that would suffice in representing a particular task related to the use of the sentence itself, for example, a model of similarity among sentences/words, a model of Twitter user connectivity, and demographics of tweets model. The use of word embedding is a handful to the sentiment analysis research because it helps build a mathematical-friendly model from sentences. The model then will be suitable as feeds for the other computational process

Knowledge management and social media: A scientometrics survey

The purpose of this research is to study the role of the social media for knowledge sharing. The study presents a comprehensive review of the researches associated with the effect of knowledge management in social media. The study uses Scopus database as a primary search engine and covers 1858 of highly cited articles over the period 1994-2019. The records are statistically analyzed and categorized in terms of various criteria using an open source software package named R. The findings show that researches have grown exponentially during the recent years and the trend has continued at relatively stable rates. Based on the survey, knowledge management is the keyword which has carried the highest citations followed by social media and social networking. Among the most cited articles, papers published by researchers in United States have received the highest citations, followed by United Kingdom and China