Dr. Shashank S Singh

INFLUENCE MAXIMIZATION

Influence Maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. Social network can be represented as graph G(V,E), where node V and edges E represents users and relationship among users. For example, in the Blogosphere, it means selecting a set of blogs and websites that broadcast the information in a broad range of others. In Epidemiology, it consists of finding a set of persons that together are most likely to spread a virus to the largest number of persons in the population. Identifying these individuals can help controlling the disease transmission in the network. Finally, in Viral marketing, the problem reduces to finding a set of trendsetters that absorb the most number of customers to adopt a product.

Steps in Influence Maximization Process:

Detecting Interesting Topics
Modeling Diffusion Processes and
Identifying Influential Spreaders

Related Publication:

Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, and Bhaskar Biswas, "ACO-IM: Maximizing influence in social networks using ant colony optimization", in Soft Computing, Springer, 2019, ISSN: 1433-7479.
Shashank Sheshar Singh, Ajay Kumar, Kuldeep Singh, and Bhaskar Biswas, "LAPSO-IM: A learning-based influence maximization approach for social networks", in Applied Soft Computing, Elsevier, 2019, ISSN: 1568-4946.
Shashank Sheshar Singh, Ajay Kumar, Kuldeep Singh, and Bhaskar Biswas, "IM-SSO: Maximizing influence in social networks using social spider optimization", in Concurrency and Computation: Practice and Experience, Wiley, 2019, ISSN: 1532-0634.
Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, and Bhaskar Biswas, "MIM2: Multiple Influence Maximization across Multiple Social Networks", in Physica A: Statistical Mechanics and its Applications, Elsevier, 2019, ISSN: 0378-4371.
Shashank Sheshar Singh, Ajay Kumar, Kuldeep Singh, and Bhaskar Biswas, "C2IM: Community based context-aware influence maximization in social networks", in Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514, pp. 796 – 818, 2019, ISSN: 0378-4371.
Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, and Bhaskar Biswas, "Influence Maximization on Social Networks: A Study", in Recent Patents on Computer Science, Bentham Science, 2019, ISSN: 1874-4796 .
Shashank Sheshar Singh, Ajay Kumar, Shivansh Mishra, Kuldeep Singh,and Bhaskar Biswas, "A Centrality Measure for Influence Maximization across Multiple Social Networks", in "Advanced Informatics for Computing Research", Springer, Singapore, 2019.
Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, and Bhaskar Biswas, "CoIM: Community-Based Influence Maximization in Social Networks", in "Advanced Informatics for Computing Research", Springer, pp. 440-453, Singapore, 2018.
Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, Harish Kumar Shakya, and Bhaskar Biswas, "A Survey on Information Diffusion Models in Social Networks", in "Advanced Informatics for Computing Research", Springer, pp. 426–439, Singapore, 2018.
Shashank Sheshar Singh, Ajay Kumar, Shivansh Mishra, Kuldeep Singh, and Bhaskar Biswas, "Influence Maximization in Social Networks", in Optimization in Large Scale Problems, Springer Switzerland, 2019.

LINK PREDICTION

Many complex social, biological, and information systems are described by networks, where nodes represent individuals, and links indicate the relationships or interactions between nodes. The links include friendships among people, connections in an on-line social network, physical interactions among proteins or genes, etc. The problem of predicting missing links of a network (or the future structure of the network) from the observed structure is called link prediction, which is one of the important tasks of link mining in the data mining field. Link prediction helps us not only to understand the evolution mechanism of the complex networks theoretically, but also to solve very important issues in real-world applications.

Related Publication:

Shivansh Mishra, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas, "MNRELP-MUL: Merged Node and Edge Relevance based Link Prediction in Multiplex Networks", in Journal of Computational Science, Elsevier, 2022.
Ajay Kumar, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas, "Link Prediction Techniques, Applications, and Performance: A Survey", in Physica A: Statistical Mechanics and its Applications, Elsevier, 2020, ISSN: 0378-4371.
Shashank Sheshar Singh, Shivansh Mishra, Ajay Kumar, and Bhaskar Biswas, "CLP-ID: Community-based Link Prediction using Information Diffusion Perspective", in Information Sciences, Elsevier, 2019, ISSN: 0020-0255.
Ajay Kumar, Shivansh Mishra, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas, "Link Prediction in Complex Networks Based on Significance of Higher-Order Path Index (SHOPI)", in Physica A: Statistical Mechanics and its Applications, Elsevier, 2019, ISSN: 0378-4371.
Ajay Kumar, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas, "Level-2 node clustering coefficient-based link prediction", in Applied Intelligence, Springer, 2019, ISSN: 1573-7497 .

HIGH UTILITY ITEMSETS MINING

High utility itemsets (HUIs) mining is an important area of frequent itemsets mining (FIM). FIM mines the itemsets, which frequently occur together, in a transaction dataset. FIM algorithms do not consider the quantity and importance (unit profit, price, risk, cost and weight, etc.) of the items. Thus, an item may only appear once or zero time in a transaction. For example, if a customer buys five breads, ten breads or twenty breads, it is viewed as the same. However, in real-life, the customers buy items with some quantity. Therefore, supermarket and retailers are interested in finding the higher profitable itemsets rather than frequent itemsets. To overcome this problem, HUIs mining algorithms have been proposed. In HUIs mining, items can have the quantity and relative importance. HUIs have numerous applications such as market basket analysis, website click stream, cross-marketing in retail stores, biomedical applications, and mobile commerce applications.

Related Publication:

Kuldeep Singh, Ajay Kumar, Shashank Sheshar Singh, Harish Kumar Shakya, and Bhaskar Biswas, "EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints", in Information Sciences, Elsevier, 2019, ISSN: 0020-0255 .
Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas, "Mining of Closed High Utility Itemsets: A Survey", in Recent Patents on Computer Science, Bentham Science, 2019, ISSN: 1874-4796 .
Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas, "TKEH: an efficient algorithm for mining top-k high utility itemsets", in Applied Intelligence, Springer, 2018, ISSN: 1573-7497.
Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas, "High utility itemsets mining with negative utility value: A survey", in Journal of Intelligence and Fuzzy Systems (JIFS), IOS Press, 2018, ISSN: 1064-1246.

COMMUNITY DETECTION

Complex relationships within the data are modeled as information network in various application areas of data mining. Identification of connected groups of nodes associated with similar information is one of the common interests behind such modeling. These connected groups are referred as community structure. From the expert systems view point, community detection problem can be treated as unsupervised learning of connectivity patterns in the network data. Discovery of such connectivity patterns has a significant role in understanding the relationships among various entities of the network and their functions in a complex network. Communities are the groups of entities of the network those are similar and connected with each other. Similarity among entities of network is defined in terms of connectivity. Generally, dense connectivity represents high similarity. Thus, community detection problem is nothing but a clustering problem in network. Often, community detection problem is referred as graph clustering.

Related Publication:

Sneha Mishra, Shashank Sheshar Singh, Shivansh Mishra, and Bhaskar Biswas, "TCD2: Tree-based community detection in dynamic social networks", in Expert Systems With Applications, Elsevier, 2020, ISSN: 0957-4174.
Harish Kumar Shakya, Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas, "Identifying Community Structure in Social Networks Using Node Similarity Based Permanence Concept" in Journal of Advanced Research in Dynamical and Control Systems, 2018, ISSN: 1943-023X.