Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

Recent studies show that by combining network topology and node attributes, we can better understand community structures in complex networks. However, existing algorithms do not explore "contextually" similar node attribute values, and therefore may miss communities defined with abstract concepts. We propose a community detection and characterization algorithm that incorporates the contextual information of node attributes described by multiple domain-specific hierarchical concept graphs. The core problem is to find the context that can best summarize the nodes in communities, while also discovering communities aligned with the context summarizing communities. We formulate the two intertwined problems, optimal community-context computation, and community discovery, with a coordinate-ascent based algorithm that iteratively updates the nodes' community label assignment with a community-context and computes the best context summarizing nodes of each community. Our unique contributions include (1) a composite metric on Informativeness and Purity criteria in searching for the best context summarizing nodes of a community; (2) a node similarity measure that incorporates the context-level similarity on multiple node attributes; and (3) an integrated algorithm that drives community structure discovery by appropriately weighing edges. Experimental results on public datasets show nearly 20 percent improvement on F-measure and Jaccard for discovering underlying community structure over the current state-of-the-art of community detection methods. Community structure characterization was also accurate to find appropriate community types for four datasets. Moreover, our algorithm yields insightful community structures that explain the contextual relationships among communities, which helps us better understand two real-world applications of social networks.

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The recent global outbreak of the coronavirus disease has spread to all corners of the globe, introducing numerous social challenges. Twitter platforms have been used to identify public opinion about events at the local and global scale. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1 st , 2020 to April 30 th , 2020 and explored topic modeling to identify the most discussed topics and themes during this period. Additionally, we analyzed the temporal changes in the topics with respect to the events that occurred. We found eight topics were sufficient to identify the themes in our corpus. The dominant topics were found to vary over time and align with the events related to the COVID-19 pandemic.

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