People frequently use social media to find information that may not be readily available from search engines or to request input from their peers. However, the search technology built into conventional social media platforms is not designed for information retrieval purposes. Since content is not archived, it is difficult to find answers to similar...

The immense growth of social media has shifted online platforms into effective resources for determining and categorizing potential customers. Relational learning (RL) adopts network connectivity and social media content to establish predictions about users. It uses the influences of social network structures and user content to model a supervised learning...

The explosive and viral nature of social media creates massive amounts of high-dimensional data. Features such as hashtags, keywords, or slang words are created every day and quickly become popular within a short period of time. However, social media data is not fully structured and its features are usually not predefined. One way to resolve this problem...

Background Attributed networks are networks whose nodes contain rich sets of features or attributes. In social networks, for instance, attributes may be user interests, while for paper citation networks they may be papers associated with an institution or scholar. The pervasiveness of attributed networks across numerous domains has given rise to the...