Entropy based flow transfer for influence dissemination in networks

Saxena, Chandni; Doja, M. N.; Ahmad, Tanvir

doi:10.1016/j.physa.2020.124630

Cited by 3 publications

(8 citation statements)

References 38 publications

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“…Information entropy may also be used as a centrality metric to rank nodes (or edges) by importance. In this sense, several authors [ 14 , 17 , 19 , 20 , 21 , 23 , 29 , 30 , 32 , 33 , 36 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] developed a number of node entropy metrics and provided the appropriate interpretation. Like for graph entropy metrics, many of these are constructed equivalently to the use of information functionals (see Equation ( 9 )) [ 20 , 21 , 44 , 48 , 51 , 56 , 59 , 60 , 61 , 62 ].…”

Section: Resultsmentioning

confidence: 99%

“…Like for graph entropy metrics, many of these are constructed equivalently to the use of information functionals (see Equation ( 9 )) [ 20 , 21 , 44 , 48 , 51 , 56 , 59 , 60 , 61 , 62 ]. Node entropy metrics are based on degree [ 21 , 32 , 56 , 57 , 59 , 60 , 62 , 63 ], neighbor degree [ 59 ] or strength [ 57 ], the weight of edges [ 56 , 60 ], betweenness [ 21 , 33 ], closeness centrality [ 21 ], paths [ 14 , 17 , 54 ], or walks [ 19 , 23 , 29 , 30 ], or less traditional metrics, such as the topological potential [ 20 ], the probability of information flow [ 36 ], the probability of a protein complex [ 55 ], the number of nodes [ 58 ], or protein annotations [ 61 ]. In many cases, locality is important and, thus, the properties of the neighbors are taken into account [ 57 , 60 , 62 , 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…“Various centrality metrics establish different aspects for the meaning of an actor to be central to the network.” [ 63 ]…”

Section: Resultsmentioning

confidence: 99%

“…By far, the largest number of entropy metrics are based on node degree [ 16 , 21 , 31 , 39 , 41 , 42 , 49 , 56 , 62 ], its extension to weighted graphs, i.e., node strength [ 38 , 56 ], the degree and/or strength of neighbors of a node [ 32 , 43 , 57 , 59 , 60 , 63 ], or degrees associated to a subgraph of a node [ 44 , 48 ]. Yet, the definition of the probability distribution varies from one author to another.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, Saxena et al [ 63 ] based the entropy metric on the node’s degree as well as the degree of its neighbors, as follows:

. For this probability distribution,

is the degree of node j , which is a neighbor of node i , thus

.…”

Section: Resultsmentioning

confidence: 99%

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A Survey of Information Entropy Metrics for Complex Networks

Omar

Plapper

2020

Entropy

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Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…“Various centrality metrics establish different aspects for the meaning of an actor to be central to the network.” [ 63 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Finally, Saxena et al [ 63 ] based the entropy metric on the node’s degree as well as the degree of its neighbors, as follows:

. For this probability distribution,

is the degree of node j , which is a neighbor of node i , thus

.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Information Entropy Metrics for Complex Networks

Omar

Plapper

2020

Entropy

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Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

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Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

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Identifying vital nodes in complex networks based on information entropy, minimum dominating set and distance

Chen

2021

Int. J. Mod. Phys. B

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Identifying the vital nodes in complex networks is essential in disease transmission control and network attack protection. In this paper, in order to identify the vital nodes, we define a centrality method named EMDC, which is based on information entropy, minimum dominating set (MDS) and the distance between node pairs. This method calculates the local spreading capability (LSC) of node by information entropy and selects that nodes have the largest value of LSC as core nodes by MDS. Then it defines the node’s spreading capability (SC) to use the sum of weighted distances from a node to the core nodes. Finally, the nodes are ranked by considering SC of their neighbors. The key nodes can be further identified in complex networks. In order to verify the effectiveness of this method, key nodes identification simulation experiments are carried out on 11 real networks, Scale-Free (BA) networks and Small-World (WS) networks, respectively. Experimental results show that this method can more effectively identify the influence of nodes in the networks.

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Entropy based flow transfer for influence dissemination in networks

Cited by 3 publications

References 38 publications

A Survey of Information Entropy Metrics for Complex Networks

A Survey of Information Entropy Metrics for Complex Networks

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Identifying vital nodes in complex networks based on information entropy, minimum dominating set and distance

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