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Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.
Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GNNs) have shown their superiority over traditional ones such as label p
The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for s
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is desired. Yet, their different foci, modeling approaches, and sparse over
In the present day, more than 3.8 billion people around the world actively use social media. The effectiveness of social media in facilitating quick and easy sharing of information has attracted brands and advertizers who wish to use the platform to
We study the extent to which we can infer users geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring. The challenge