arXiv subjects

Muhammad Qasim Pasta

Publications and source records attributed to Muhammad Qasim Pasta.

5 recordsLinked to original sources

arXiv2024

Exploring the Role of Network Centrality in Player Selection: A Case Study of Pakistan Super League

Cricket, a popular bat-and-ball game in South Asia, is played between two 11-player teams. The Pakistan Super League (PSL) is a commercial T20 domestic league comprised of six franchise-owned teams, where player selection is competitive. In this study, an existing role-based ranking structure is assessed that evaluates player performance in the context of team belongingness to generate optimal Pakistan cricket teams for international tournaments. The underlying assumption is that since cricket is fundamentally a team sport, the performance of players compared to their peers plays a crucial role in their selection. To accomplish this, a network is generated using ball-by-ball data from previous PSL matches (2016-2022), and social network analysis (SNA) techniques such as centrality and clustering coefficient measures, are employed to quantify the level of belongingness among Pakistani cricket players within the PSL network. Characteristic network models, such as the Erd\"os-R\'enyi, Watts-Strogatz, and Barab\'asi-Albert models are utilized to gain insights into the small-world properties of the network. By ranking players using centrality and clustering coefficient metrics, four teams are formulated, and these teams are subsequently compared to the official squad selected by the Pakistan Cricket Board (PCB) for the recent ICC Men's T20 World Cup in 2022. This evaluation sheds light on the allegations of nepotism and favoritism in team formations that have been attributed to the PCB over the years. Based on our findings, out of the 18 players in the World Cup squad, 11 were included in the teams we formed. While most of the 7 players who were not included in our teams were still selected for the ICC Men's T20 World Cup 2022, they ranked highly in our rankings, suggesting their potential and competence.

cs.SI↗

arXiv2017

Generating online social networks based on socio-demographic attributes

Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed due to an individual's demographic attributes such as age group, gender, geographic location, or due to a network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics. A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model network formation. We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model. The proposed model is flexible and thus can generate networks with varying demographic and structural properties.

cs.SI↗

arXiv2017

Leveraging Evolution Dynamics to Generate Benchmark Complex Networks with Community Structures

The past decade has seen tremendous growth in the field of Complex Social Networks. Several network generation models have been extensively studied to develop an understanding of how real world networks evolve over time. Two important applications of these models are to study the evolution dynamics and processes that shape a network, and to generate benchmark networks with known community structures. Research has been conducted in both these directions, relatively independent of the other. This creates a disjunct between real world networks and the networks generated as benchmarks to study community detection algorithms. In this paper, we propose to study both these application areas together. We introduce a network generation model which is based on evolution dynamics of real world networks and, it can generate networks with community structures that can be used as benchmark graphs. We study the behaviour of different community detection algorithms based on the proposed model and compare it with other models to generate benchmark graphs. Results suggest that the proposed model can generate networks which are not only structurally similar to real world networks but can be used to generate networks with varying community sizes and topologies.

cs.SI↗

arXiv2016

On Varying Topology of Complex Networks and Performance Limitations of Community Detection Algorithms

One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are interested in the size of networks, the size of community structures, the average connectivity of nodes and the ratio of inter-intra cluster edges. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms that fail to incorporate varying topological features of different networks.

cs.SI↗

arXiv2013

Demographic and Structural Characteristics to Rationalize Link Formation in Online Social Networks

Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed either due to an individual's demographic profile such as age group, gender, geographic location or due to network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics. A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model growth of a network.We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model.

cs.SI↗