Complexity and algorithms for neighbor-sum-2-distinguishing {1,3}-edge-weighting of graphs

2022 ◽  
Author(s):  
B.S. Panda ◽  
Priyamvada
Keyword(s):  
Edge Weighting

Improving Color Constancy by Photometric Edge Weighting

2012 ◽  
Vol 34 (5) ◽  
pp. 918-929 ◽  
Author(s):  
A. Gijsenij ◽  
T. Gevers ◽  
J. van de Weijer
Keyword(s):  
Color Constancy ◽  
Edge Weighting

scholarly journals The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

scholarly journals Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Theodore R. Gibbons ◽  
Stephen M. Mount ◽  
Endymion D. Cooper ◽  
Charles F. Delwiche
Keyword(s):  
Clustering Algorithm ◽  
Edge Weighting ◽  
Homology Inference ◽  
Markov Clustering

On the Aggression Diffusion Modeling and Minimization in Twitter

2022 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Marinos Poiitis ◽  
Athena Vakali ◽  
Nicolas Kourtellis
Keyword(s):  
Online Social Networks ◽  
Important Research ◽  
Diffusion Modeling ◽  
Propagation Modeling ◽  
Twitter Data ◽  
Edge Weighting ◽  
Linear Threshold ◽  
Important Research Topic ◽  
The Social ◽  
Independent Cascade

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Exploring activity-driven network with biased walks

2017 ◽  
Vol 28 (09) ◽  
pp. 1750111
Author(s):  
Yan Wang ◽  
Ding Juan Wu ◽  
Fang Lv ◽  
Meng Long Su
Keyword(s):  
Random Walk ◽  
Diffusion Process ◽  
Random Walks ◽  
Stationary Distribution ◽  
Significant Role ◽  
Search Strategy ◽  
Transition Probability ◽  
Edge Weighting ◽  
Coverage Function ◽  
Analytical Expressions

We investigate the concurrent dynamics of biased random walks and the activity-driven network, where the preferential transition probability is in terms of the edge-weighting parameter. We also obtain the analytical expressions for stationary distribution and the coverage function in directed and undirected networks, all of which depend on the weight parameter. Appropriately adjusting this parameter, more effective search strategy can be obtained when compared with the unbiased random walk, whether in directed or undirected networks. Since network weights play a significant role in the diffusion process.

scholarly journals Adaptive edge weighting for graph-based learning algorithms

2016 ◽  
Vol 106 (2) ◽  
pp. 307-335 ◽  
Author(s):  
Masayuki Karasuyama ◽  
Hiroshi Mamitsuka
Keyword(s):  
Learning Algorithms ◽  
Edge Weighting ◽  
Graph Based Learning

A novel image segmentation model with an edge weighting function

2013 ◽  
Vol 8 (1) ◽  
pp. 121-132 ◽  
Author(s):  
Wen Juan Zhang ◽  
Xiang Chu Feng ◽  
Yu Han
Keyword(s):  
Image Segmentation ◽  
Weighting Function ◽  
Edge Weighting

scholarly journals On vertex-coloring edge-weighting of graphs

2009 ◽  
Vol 4 (2) ◽  
pp. 325-334
Author(s):  
Hongliang Lu ◽  
Xu Yang ◽  
Qinglin Yu
Keyword(s):  
Vertex Coloring ◽  
Edge Weighting

scholarly journals EDGE-WEIGHTING OF GENE EXPRESSION GRAPHS

2010 ◽  
Vol 13 (02) ◽  
pp. 217-238 ◽  
Author(s):  
GRAINNE KERR ◽  
DIMITRI PERRIN ◽  
HEATHER J. RUSKIN ◽  
MARTIN CRANE
Keyword(s):  
Gene Expression ◽  
Missing Values ◽  
Graphical Representation ◽  
Search Algorithm ◽  
Expression Profiles ◽  
Edge Weighting ◽  
Micro Array ◽  
Clustering And Classification ◽  
Discrimination Parameter ◽  
Classification Procedures

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed.

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