arXiv subjects

Faraz Zaidi

Publications and source records attributed to Faraz Zaidi.

12 recordsLinked to original sources

arXiv2024

Economic Hubs and the Domination of Inter-Regional Ties in World City Networks

Cities are widely considered the lifeblood of a nations economy housing the bulk of industries, commercial and trade activities, and employment opportunities. Within this economic context, multinational corporations play an important role in this economic development of cities in particular, and subsequently the countries and regions they belong to, in general. As multinational companies are spread throughout the world by virtue of ownership-subsidiary relationship, these ties create complex inter-dependent networks of cities that shape and define socio-economic status, as well as macro-regional influences impacting the world economy. In this paper, we study these networks of cities formed as a result of ties between multinational firms. We analyze these networks using intra-regional, inter-regional and hybrid ties (conglomerate integration) as spatial motifs defined by geographic delineation of world's economic regions. We attempt to understand how global cities position themselves in spatial and economic geographies and how their ties promote regional integration along with global expansion for sustainable growth and economic development. We study these networks over four time periods from 2010 to 2019 and discover interesting trends and patterns. The most significant result is the domination of inter-regional motifs representing cross regional ties among cities rather than national and regional integration.

physics.soc-ph↗

arXiv2023

World City Networks and Multinational Firms: An Analysis of Economic Ties Over a Decade

One perspective to view the economic development of cities is through the presence of multinational firms; how subsidiaries of various organizations are set up throughout the globe and how cities are connected to each other through these networks of multinational firms. Analysis of these networks can reveal interesting economical and spatial trends, as well as help us understand the importance of cities in national and regional economic development. This paper aims to study networks of cities formed due to the linkages of multinational firms over a decade (from 2010 to 2019). More specifically we are interested in analyzing the growth and stability of various cities in terms of the connections they form with other cities over time. Our results can be summarized into two key findings: First, we ascertain the central position of several cities due to their economically stable connections; Second, we successfully identify cities that have evolved over the past decade as the presence of multinational firms has increased in these cities.

cs.SI↗

arXiv2023

Topological Filtering for Visual Data Mining and Analysis of Complex Networks

The discovery of small world and scale free properties of many real world networks has revolutionized the way we study, analyze, model and process networks. An important way to analyze these complex networks is to visualize them using graph layout algorithms. Due to their large size and complex connectivity, it is difficult to make deductions from the visual representation of these networks. In this paper, we present a method for interactive analysis of large graphs based on topological filtering, network metrics and visualization. We analyze a number of real world networks and draw interesting conclusions using the proposed method.

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↗

arXiv2014

Tunable and Growing Network Generation Model with Community Structures

Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.

cs.SI↗

arXiv2014

Resilience of Social Networks Under Different Attack Strategies

Recent years have seen the world become a closely connected society with the emergence of different types of social networks. Online social networks have provided a way to bridge long distances and establish numerous communication channels which were not possible earlier. These networks exhibit interesting behavior under intentional attacks and random failures where different structural properties influence the resilience in different ways. In this paper, we perform two sets of experiments and draw conclusions from the results pertaining to the resilience of social networks. The first experiment performs a comparative analysis of four different classes of networks namely small world networks, scale free networks, small world-scale free networks and random networks with four semantically different social networks under different attack strategies. The second experiment compares the resilience of these semantically different social networks under different attack strategies. Empirical analysis reveals interesting behavior of different classes of networks with different attack strategies.

cs.SI↗

arXiv2014

Are all Social Networks Structurally Similar? A Comparative Study using Network Statistics and Metrics

The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar. We also review the snowball sampling method and show its vulnerability against different network metrics.

cs.SI↗

arXiv2014

Analysis and Visualization of Dynamic Networks

This chapter provides an overview of the different techniques and methods that exist for the analysis and visualization of dynamic networks. Basic definitions and formal notations are discussed and important references are cited. A major reason for the popularity of the field of dynamic networks is its applicability in a number of diverse fields. The field of dynamic networks is in its infancy and there are so many avenues that need to be explored. From developing network generation models to developing temporal metrics and measures, from structural analysis to visual analysis, there is room for further exploration in almost every dimension where dynamic networks are studied. Recently, with the availability of dynamic data from various fields, the empirical study and experimentation with real data sets has also helped maturate the field. Furthermore, researchers have started to develop foundations and theories based on these datasets which in turn has resulted lots of activity among research communities.

cs.SI↗

arXiv2014

Communities and Hierarchical Structures in Dynamic Social Networks: Analysis and Visualization

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals that play important roles in a society such as Managers and Decision Makers. Detection and Visualization of these networks changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in a social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social 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↗