scholarly journals Key Node Ranking in Complex Networks: A Novel Entropy and Mutual Information-Based Approach

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 52 ◽  
Author(s):  
Yichuan Li ◽  
Weihong Cai ◽  
Yao Li ◽  
Xin Du
Keyword(s):  
Network Analysis ◽  
Mutual Information ◽  
Real World ◽  
Large Scale ◽  
Local Information ◽  
Global Information ◽  
Network Characteristics ◽  
Node Ranking ◽  
Digital Network ◽  
Key Nodes

Numerous problems in many fields can be solved effectively through the approach of modeling by complex network analysis. Finding key nodes is one of the most important and challenging problems in network analysis. In previous studies, methods have been proposed to identify key nodes. However, they rely mainly on a limited field of local information, lack large-scale access to global information, and are also usually NP-hard. In this paper, a novel entropy and mutual information-based centrality approach (EMI) is proposed, which attempts to capture a far wider range and a greater abundance of information for assessing how vital a node is. We have developed countermeasures to assess the influence of nodes: EMI is no longer confined to neighbor nodes, and both topological and digital network characteristics are taken into account. We employ mutual information to fix a flaw that exists in many methods. Experiments on real-world connected networks demonstrate the outstanding performance of the proposed approach in both correctness and efficiency as compared with previous approaches.

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

scholarly journals Local dependency in networks

2015 ◽  
Vol 25 (2) ◽  
pp. 281-293 ◽  
Author(s):  
Miloš Kudĕlka ◽  
Šárka Zehnalová ◽  
Zdenĕk Horák ◽  
Pavel Krömer ◽  
Václav Snášel
Keyword(s):  
Real World ◽  
Large Scale ◽  
Real World Data ◽  
High Quality ◽  
High Effectiveness ◽  
Local Dependency ◽  
Node Ranking ◽  
Large Scale Networks ◽  
The Relationship ◽  
Entire Network

Abstract Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.

Initial Use of Foreground Objects in Understanding Visual Scenes

2016 ◽  
Author(s):  
Catherine Jee
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Response Rate ◽  
Target Object ◽  
Local Information ◽  
Background Information ◽  
Living Room ◽  
Global Information ◽  
Scene Gist ◽  
Coffee Table

The present study examined how we recognize real‐world scenes. Previous studies suggested that, in a single glance, we could extract enough information from a visual scene to understand a scene’s category (e.g., kitchen, living room, bedroom). However, it is yet unclear what type of visual information leads to this understanding. The experiment investigated whether global information from the whole scene or local information from individual objects is critical. Global information refers to large‐scale, immovable structures in the background of a scene (e.g. kitchen cabinets). Local information refers to smaller‐scale, movable objects in the foreground of a scene (e.g. kitchen table). Participants were briefly presented with a scene in which the set of foreground objects belonged to one scene type, and the background belonged to another scene type. After the presentation of the scene, a name of the target object that was consistent with either the background (e.g. blender in kitchen) or consistent with the background (e.g. coffee table) was presented. Participants decided whether the target object is likely to appear in the scene. If local foreground information was initially used to activate scene gist, there should be a higher response rate towards the foreground‐consistent target. If global background information was initially used, there should be a higher response rate towards the background‐consistent target. Preliminary results suggest that participants used the foreground‐consistent target objects more often. This suggests that we may initially use local information to understand scenes.

Do You Want to See the Tree? Ignore the Forest

2014 ◽  
Vol 61 (3) ◽  
pp. 205-214 ◽  
Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  
Keyword(s):  
Visual Recognition ◽  
Local Level ◽  
Local Information ◽  
Visual Environment ◽  
Global Information ◽  
Focus Attention ◽  
Global Level ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽  
2021 ◽  
pp. 102442
Author(s):  
Lin Zhou ◽  
Lu Zhen ◽  
Roberto Baldacci ◽  
Marco Boschetti ◽  
Ying Dai ◽  
...  
Keyword(s):  
Heuristic Algorithm ◽  
Real World ◽  
Design Problem ◽  
Large Scale ◽  
Territory Design

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Sign in / Sign up

Export Citation Format

Share Document