A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is `application/pdf`

.

## Filters

##
###
Probably Optimal Graph Motifs

2013
*
Symposium on Theoretical Aspects of Computer Science
*

Björklund, Kaski, Kowalik ()

doi:10.4230/lipics.stacs.2013.20
dblp:conf/stacs/BjorklundKK13
fatcat:ylck4vhkzrhnvjjzwqlommvqty
*Probably**Optimal**Graph**Motifs*STACS, Kiel, 28.02.2013 15 / 24 back. ... Björklund, Kaski, Kowalik ()*Probably**Optimal**Graph**Motifs*STACS, Kiel, 28.02.2013 3 / 24 What if there is no*motif*in the*graph*? Is there something close to the*motif*? ...##
###
Probably Optimal Graph Motifs *

unpublished

The Closest

fatcat:iuokhmexobamxfdkxb3wn6kxyq
*Graph**Motif*problem encompasses several previously studied*optimization*variants, like Maximum*Graph**Motif*, Min-Substitute, and Min-Add. ... We also introduce a new*optimization*variant of the problem, called Closest*Graph**Motif*and solve it within the same time bound. ...*Probably**Optimal**Graph**Motifs*Let W = (T, h) be a branching walk in G, let s : V (T ) → D be a consistent shading of W , and let : V (T ) → {1, 2, . . . , k} be a labelling of W . ...##
###
Motif-Driven Contrastive Learning of Graph Representations
[article]

2021
*
arXiv
*
pre-print

Our framework

arXiv:2012.12533v3
fatcat:kknuji2x7zek7bsy7ziuiv5byu
*MotIf*-driven Contrastive leaRning Of*Graph*representations (MICRO-*Graph*) can: 1) use GNNs to extract*motifs*from large*graph*datasets; 2) leverage learned*motifs*to sample informative subgraphs ... To solve it, we propose to learn*graph**motifs*, which are frequently-occurring subgraph patterns (e.g. functional groups of molecules), for better subgraph sampling. ... In the E-step we calculate the posterior*probability*, = ( = | ). In the M-step, we*optimize*the objective to find the*optimal*partition and parameter . ...##
###
A combinatorial optimization approach for diverse motif finding applications

2006
*
Algorithms for Molecular Biology
*

Results: We introduce a versatile combinatorial

doi:10.1186/1748-7188-1-13
pmid:16916460
pmcid:PMC1570465
fatcat:yfk3tic2y5a2rcwrxs35a6qycy
*optimization*framework for*motif*finding that couples*graph*pruning techniques with a novel integer linear programming formulation. ... Moreover, in most cases, our approach finds provably*optimal*solutions to the underlying*optimization*problem. ... Acknowledgements The authors thank Stephen Altschul for help in assessing the statistical significance of discovered*motifs*, and the anonymous referees for their helpful suggestions. ...##
###
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
[article]

2022
*
arXiv
*
pre-print

Recently,

arXiv:2209.07924v1
fatcat:fpidyeugb5aqpnnmwgka75y6xa
*Graph*Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on*graphs*. ... More specifically, with continuous relaxation of*graphs*and the reparameterization trick, GNNInterpreter learns a probabilistic generative*graph*distribution which produces the most representative*graph*... Table 9 : 9 For each*motif*set, the average predicted class*probability*of each class is computed by averaging over 5000*graphs*containing the corresponding*motif*. ...##
###
Topological motifs populate complex networks through grouped attachment

2018
*
Scientific Reports
*

: the Erdös-Rényi, small-world, scale-free, popularity-similarity-

doi:10.1038/s41598-018-30845-4
pmid:30140017
pmcid:PMC6107624
fatcat:id6xekreezheloarahz44e5t4e
*optimization*, and nonuniform popularity-similarity-*optimization*models. ... Recently, two hyperbolic geometrical models have been developed: popularity-similarity-*optimization*(PSO) 7 , and nonuniform popularity-similarity-*optimization*(nPSO) 8,9 models. ... Among the nodes in the*graph*G, we select nodes with a*probability*p/q to be connected, and for every selected nodes, we connect to the nodes in the*graph*F with a*probability*q (p < q < 1). ...##
###
MotifCut: regulatory motifs finding with maximum density subgraphs

2006
*
Bioinformatics
*

Results: We present MotifCut, a

doi:10.1093/bioinformatics/btl243
pmid:16873465
fatcat:wwfrirzimfal3lpqefhdz4qtli
*graph*-theoretic approach to*motif*finding leading to a convex*optimization*problem with a polynomial time solution. ... Most existing methods formulate*motif*finding as an intractable*optimization*problem and rely either on expectation maximization (EM) or on local heuristic searches. ... The*probability*of a k-mer belonging to a*motif*given its mutation distance to a*motif*k-mer. ...##
###
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification
[article]

2020
*
arXiv
*
pre-print

To improve this, we introduce data augmentation on

arXiv:2007.05700v2
fatcat:m3marhsgljemxflwydlqjwyk3i
*graphs*(i.e.*graph*augmentation) and present four methods:random mapping, vertex-similarity mapping,*motif*-random mapping and*motif*-similarity mapping ... Furthermore, we propose a generic model evolution framework, named M-Evolve, which combines*graph*augmentation, data filtration and model retraining to*optimize*pre-trained*graph*classifiers. ...*Motif*-Random Mapping*Graph**motifs*are sub-*graphs*that repeat themselves in a specific*graph*or even among various*graphs*. ...##
###
Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

2011
*
BMC Genomics
*

The unpaired

doi:10.1186/1471-2164-12-s5-s8
pmid:22369183
pmcid:PMC3287504
fatcat:vtljbdv2xvdrvc26jyaqnawgra
*probabilities*, the*probabilities*of not forming pairs, are significantly higher than negative controls and the flanking sequence surrounding the binding site, indicating that SRSF1 proteins ... Conclusion: In this study, we presented a computational model to predict the sequence consensus and*optimal*RNA secondary structure for protein-RNA binding regions. ... base pairing*probability*of k bases in the*motif*θ = (θ i ); and 4) the penalty for the deviation from the*optimal*base pairing*probability*a. ...##
###
MOTIF-Driven Contrastive Learning of Graph Representations

2021
*
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
*

We propose a

doi:10.1609/aaai.v35i18.17986
fatcat:67kxuaqvvvenvepjow4jbdsd3m
*MOTIF*-driven contrastive framework to pretrain a*graph*neural network in a self-supervised manner so that it can automatically mine*motifs*from large*graph*datasets. ... Our framework achieves state-of-the-art results on various*graph*-level downstream tasks with few labels, like molecular property prediction. ... We predict the*probabilities*of each subgraph belonging to the*optimal*clusters via representation-center similarity and learn subgraph representations to be more similar or dissimilar to the appropriate ...##
###
Inherent limits on optimization and discovery in physical systems

2014
*
Annals of Physics
*

We find inherent limits to the potential for

doi:10.1016/j.aop.2014.10.008
fatcat:j57pjeezxjan5kwvnhri2mzsvi
*optimization*of a given system and its approximate representations by*motifs*, and the ability to reconstruct the full system given approximate representations ... Topological mapping of a large physical system on a*graph*, and its decomposition using universal measures is proposed. ... as: 3, 5, 17 I ve (G) = − Nm i Nm j p ij log 2 (p ij ), by assigning*probabilities*to each*motif*, where p ij is a discrete joint*probability*distribution that depends on vertices and edges within the ...##
###
Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time
[article]

2021
*
arXiv
*
pre-print

We consider the problem of sampling and approximately counting an arbitrary given

arXiv:2107.06582v2
fatcat:4u2fpjlqqbgibgh2ze3f4cxr7e
*motif*H in a*graph*G, where access to G is given via queries: degree, neighbor, and pair, as well as uniform edge sample ... We present a new algorithm for sampling and approximately counting arbitrary*motifs*which, up to poly(log n) factors, is always at least as good as previous results, and for most*graphs*G is strictly better ... There exists a*motif*H D , with an*optimal*decomposition D, and a family of*graphs*G over n vertices and m edges, as follows. ...##
###
Cyclic transitions between higher order motifs underlie sustained activity in asynchronous sparse recurrent networks
[article]

2019
*
bioRxiv
*
pre-print

If the network fails to engage the dynamical regime characterized by a recurring stable pattern of

doi:10.1101/777219
fatcat:7w6skrk66bbjbje7cil4kqmoiu
*motif*dominance, spiking activity ceased. ... We find that stereotyped low variance cyclic transitions between three isomorphic triangle*motifs*, quantified as a Markov process, are required for sustained activity. ... We searched for 467 the*optimal**probability*of connection from excitatory to inhibitory units, p e→i , and the 468*optimal**probability*of connection from inhibitory to excitatory units, p i→e , such that ...##
###
Mixed Random Sampling of Frames method for counting number of motifs
[article]

2019
*
arXiv
*
pre-print

These networks are represented in the form of directed and undirected simple

arXiv:1904.02483v1
fatcat:c7ia3h75ffa3jgmopabfe6xqpa
*graphs*. Exact calculating requires huge computational resources for such large*graphs*. ... A method for calculating the frequencies of network*motifs*using the Monte Carlo method with control of the accuracy of calculations is proposed. ... Using the value calculated by this formula in (1), we calculate the*optimal*estimate of the number n of*motif*instances in the*graph*. ...##
###
Subgraph Covers: An Information-Theoretic Approach to Motif Analysis in Networks

2014
*
Physical Review X
*

Some recently introduced random

doi:10.1103/physrevx.4.041026
fatcat:mvemsywsubcjdloy7ohgzvndsa
*graph*models that can incorporate significant densities of*motifs*have natural formulations in terms of subgraph covers and the presented approach can be used to match networks ... To prove the practical value of our approach we also present a heuristic for the resulting NP-hard*optimization*problem and give results for several real world networks. ... McKay were used in gener-ating the various isomorphism classes used in our analysis and the*graph*-tool [20] Python package developed by Tiago de Paula Peixoto for finding subgraphs and manipulating*graphs*...
« Previous

*Showing results 1 — 15 out of 33,141 results*