Graph Mining

Discovering important nodes in graph data attracted a lot of attention. Social networks are good examples of graph data in which each node represents a person and each edge represents a relationship between two people. There are several methods for the task of discovering important nodes in graph data. In this paper, important people are defined with their roles in society or organization. We propose an efficient method to discover leaders in graph network. For this purpose, both structural feature like entropy and inherent features including from, to, subject and message's time of social networks are used to propose a novel method for discovering important nodes in social networks. The proposed method was applied to Enron dataset and compared with previous methods. The proposed method succeeded to first, discover more important roles in Enron dataset, second, determine CEO as leader of Enron Corporation and third, discover two out of four CEOs among top VIPs.

The rise of the Internet and the exponential increase in data have made manual data summarization and analysis a challenging task. Instagram social network is a prominent social network widely utilized in Iran for information sharing and communication across various age groups. The inherent structure of Instagram, characterized by its text-rich content and graph-like data representation, enables the utilization of text and graph processing techniques for data analysis purposes. The degree distributions of these networks exhibit scale-free characteristics, indicating non-random growth patterns. Recently, word co-occurrence has gained attention from researchers across multiple disciplines due to its simplicity and practicality. Keyword extraction is a crucial task in natural language processing. In this study, we demonstrated that high-precision extraction of keywords from Instagram posts in the Persian language can be achieved using unsupervised word co-occurrence methods without resorting to conventional techniques such as clustering or pre-trained models. After graph visualization and community detection, it was observed that the top topics covered by news agencies are represented by these graphs. This approach is generalizable to new and diverse datasets and can provide acceptable outputs for new data. To the author's knowledge, this method has not been employed in the Persian language before on Instagram social network. The new crawled data has been publicly released on GitHub for exploration by other researchers. By employing this method, it is possible to use other graph-based algorithms, such as community detections. The results help us to identify the key role of different news agencies in information diffusion among the public, identify hidden communities, and discover latent patterns among a massive amount of data.