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Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations. [ABSTRACT FROM AUTHOR]

The online social network has become an integral part of our day to life and serves as an excellent platform for sharing ideas, opinions, and products. Influence maximization (IM) is a widely studied topic in the area of social network analysis. The objective of IM is to find influential nodes that can disseminate information to a larger extent in the network. Many local and global centrality measures are proposed to rank the nodes based on their spreading capability with certain limitations. Many proposed algorithms locate the spreaders sharing overlapping regions or are closely placed, which may cause interference in spreading. In this paper, based on the notion of maximum coverage of the information and minimum interference in spreading, we propose a novel semi-local algorithm named as modified degree centrality with exclusion ratio to identify influential nodes from diverse locations in the network. We use modified degree centrality by considering neighbours upto 2-hops and introduce the novel idea of exclusion ratio to ensure minimum overlapping between regions influenced by the chosen spreader nodes. Extensive experimental validation using classical information diffusion model demonstrates that the proposed method delivers better results in comparison to many popular contemporary methods of influence maximization. [ABSTRACT FROM AUTHOR]

Community detection is regarded as a significant research domain in social network analysis. Thanks to its merits, including linear-time complexity, performance and simplicity, the label propagation algorithm (LPA) has attracted a lot of researchers' interests in recent years. However, regarding the label propagation process, it has some drawbacks such as uncertainty and randomness behavior which may negatively impact the stability and accuracy of community detection. In this paper, a simple and fast method is proposed to overcome the LPA's drawbacks. We have proposed a novel method to improve the certainty and stability of the label propagation algorithm based on node ranking in a social network, called the CSLPR algorithm. After performing a proposed local method for ranking nodes, the label propagation is started from the nodes with lowest rank. In the first and second iterations t < = 2 of the propagation, if the number of maximum label frequency in neighboring of a node be equal, the Adamic/Adar index is used for selecting the appropriate label. For the other iterations t > 2 , a new criterion, known as label strength, is applied to select the label with the highest strength of a node. In real-world social networks, communities are established around important nodes. Therefore, when a low-significance node joins the networks, it receives the label of a community C i that has the most connection to this community. The proposed method is evaluated on several real-world and artificial networks. The results reveal that the proposed method can accurately detect communities with higher performance, certainty, and stability in comparison to other methods. [ABSTRACT FROM AUTHOR]

The different communications of users in social networks play a key role in effect to each other. The effect is important when they can achieve their goals through different communications. Studying the effect of specific users on other users has been modeled on the influence maximization problem on social networks. To solve this problem, different algorithms have been proposed that each of which has attempted to improve the influence spread and running time than other algorithms. Due to the lack of a review of the meta-heuristic algorithms for the influence maximization problem so far, in this paper, we first perform a comprehensive categorize of the presented algorithms for this problem. Then according to the efficient results and significant progress of the meta-heuristic algorithms over the last few years, we describe the comparison, advantages, and disadvantages of these algorithms. [ABSTRACT FROM AUTHOR]