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Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective

Anwesha Sengupta, Shashankaditya Upadhyay, Indranil Mukherjee, Prasanta K. Panigrahi

Abstract

Market competition has a role which is directly or indirectly associated with influential effects of individual sectors on other sectors of the economy. The present work studies the relative position of a product in the market through the identification of influential spreaders and its corresponding effect on the other sectors of the market using complex network analysis during the pre-, in-, and post-crisis induced lockdown periods using daily data of NSE from December, 2019 to June, 2021. The existing approaches using different centrality measures failed to distinguish between the positive and negative influences of the different sectors in the market which act as spreaders. To obviate this problem, this paper presents an effective measure called LIEST (Local Influential Effects for Specific Target) that can examine the positive and negative influences separately with respect to any crisis period. LIEST considers the combined impact of all possible nodes which are at most three steps away from the specific targets for the networks. The essence of non-linearity in the network dynamics without considering single node effect becomes visible particularly in the proposed network.

Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective

Abstract

Market competition has a role which is directly or indirectly associated with influential effects of individual sectors on other sectors of the economy. The present work studies the relative position of a product in the market through the identification of influential spreaders and its corresponding effect on the other sectors of the market using complex network analysis during the pre-, in-, and post-crisis induced lockdown periods using daily data of NSE from December, 2019 to June, 2021. The existing approaches using different centrality measures failed to distinguish between the positive and negative influences of the different sectors in the market which act as spreaders. To obviate this problem, this paper presents an effective measure called LIEST (Local Influential Effects for Specific Target) that can examine the positive and negative influences separately with respect to any crisis period. LIEST considers the combined impact of all possible nodes which are at most three steps away from the specific targets for the networks. The essence of non-linearity in the network dynamics without considering single node effect becomes visible particularly in the proposed network.
Paper Structure (8 sections, 16 equations, 3 figures, 5 tables)

This paper contains 8 sections, 16 equations, 3 figures, 5 tables.

Figures (3)

  • Figure 1: Methodology
  • Figure 2: Top 15 nodes using (a) Betweenness centrality measures (b) Harmonic centrality measures (c) Eigen centrality measures (d) Random walk harmonic centrality measures (e) Random walk betweenness centrality, for positive network during in-COVID time period.
  • Figure 4: Top 15 nodes using (a) Betweenness centrality measures (b) Harmonic centrality measures (c) Eigen centrality measures (d) Random walk harmonic centrality measures (e) Random walk betweenness centrality, for positive network in post-COVID time period.

Theorems & Definitions (8)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Definition 8