Novel centrality metrics for studying essentiality in protein‐protein interaction networks based on group structures

Abstract

In this work, we introduce centrality metrics based on group structures, and we show their performance in estimating importance in protein-protein interaction networks (PPINs). The centrality metrics introduced are extensions of well-known nodal metrics. However, instead of focusing on a single node, we focus on that node and the set of nodes around it. Furthermore, we require the set of nodes to induce a specific pattern or structure. The structures investigated range from the "stricter" induced stars and cliques, to a "looser" definition of a representative structure. We derive the computational complexity of all metrics and provide mixed integer programming formulations; due to the problem complexity and the size of PPINs, using commercial solvers is not always viable. Hence, we propose a combinatorial branch-and-bound solution approach. We conclude by showing the effectiveness of the proposed metrics in identifying essential proteins in Helicobacter pylori and comparing them to nodal metrics.

The critical node detection problem in hypergraphs using weighted node degree centrality

“…Possibilities for centrality metrics are large in both number and variety, but each metric has to be investigated in terms of their applicability on hypergraphs. Multiple instances of centrality metrics can be found for example in Rasti & Vogiatzis (2022) , where descriptions for multiple centrality metrics are defined such as group degree centrality, group average-closeness centrality, group betweenness centrality, representative degree centrality, clique betweenness centrality, and star closeness centrality. Other types of centralities may need to be analyzed for different types of connections, such as stochastic centralities for random networks or probabilistic connections, such as the row-stochastic centrality presented in Mostagir & Siderius (2021) .…”

Towards modelling and analysis of longitudinal social networks

“…Sizemore et al [18] work with a contact sequence where nodes remain static. In addition, the natural extension of centrality to groups and classes [19], [20] is usually omitted. Other authors propose MLI based on network embedding and machine learning (ML) [21].…”

The Star Degree Centrality Problem: A Decomposition Approach

“…This topic has been quite prominent in the optimization community, as we will discuss in the next Section. An example of how the previously defined three notions of group centrality change in the case of cliques is discussed next based on Figure 3 adapted from the works in and (Rasti and Vogiatzis, 2022).…”

“…Example 3 reveals the necessity for smartly selecting nodes to add in the structure we are building, as bigger in cardinality does not necessarily imply an improvement in its centrality. Using the definitions from (Rasti and Vogiatzis, 2022), we can assign a structure centrality value to a node. We say that the structure centrality of a node is the maximum value of the centrality of the structure the node belongs to among all structures it can be part of.…”

“…The clique that leads to the maximum centrality value is the clique centrality of that node. For more details, see the calculations and definitions for other structures (e.g., stars, representatives, quasi-cliques) and centrality metrics in (Rasti and Vogiatzis, 2022).…”