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

##
###
Probabilities of Sentences about Very Sparse Random Graphs

1992
*
Random structures & algorithms (Print)
*

We prove that for every firstorder

doi:10.1002/rsa.3240030105
fatcat:jctrrksmsffrzfpzwpd2ifqik4
*sentence*, the*probability*that the*sentence*is true for the*random**graph*has an asymptotic limit. ... W e consider*random**graphs*with edge*probability*Pn-Q, where n is the number*of*vertices*of*the*graph*, / 3 > 0 is fixed, and a = 1 or a = (I + 1)/1 for some fixed positive integer I . ...*Of*course, this gives us much more expressibility, and there are many instances where secondorder*sentences*do not have asymptotic*probabilities*(see [2]), but for*very**sparse**random**graphs*, limit laws ...##
###
Probabilities of sentences about very sparse random graphs

*
Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science
*

We prove that for every firstorder

doi:10.1109/fscs.1990.89591
dblp:conf/focs/Lynch90
fatcat:js52l2hqkrhq5gdfhcc7yrihtq
*sentence*, the*probability*that the*sentence*is true for the*random**graph*has an asymptotic limit. ... W e consider*random**graphs*with edge*probability*Pn-Q, where n is the number*of*vertices*of*the*graph*, / 3 > 0 is fixed, and a = 1 or a = (I + 1)/1 for some fixed positive integer I . ...*Of*course, this gives us much more expressibility, and there are many instances where secondorder*sentences*do not have asymptotic*probabilities*(see [2]), but for*very**sparse**random**graphs*, limit laws ...##
###
Faster Clustering via Non-Backtracking Random Walks
[article]

2017
*
arXiv
*
pre-print

This paper presents VEC-NBT, a variation on the unsupervised

arXiv:1708.07967v1
fatcat:rkylk3wbujcgdkqfwp7ilsseni
*graph*clustering technique VEC, which improves upon the performance*of*the original algorithm significantly for*sparse**graphs*. ... VEC employs a novel application*of*the state-*of*-the-art word2vec model to embed a*graph*in Euclidean space via*random*walks on the nodes*of*the*graph*. ... However, accuracy is still lower than desirable for*very**sparse**graphs*. In addition, although 60 is not an excessively long*random*walk, reducing it would speed up the algorithm. ...##
###
The logic of random regular graphs

2010
*
Journal of Combinatorics
*

The case

doi:10.4310/joc.2010.v1.n4.a3
fatcat:ygdujdrtjvf27ifwlaokq7zoeq
*of**sparse**random**graphs*has also been studied in detail for the binomial*random**graph*model. We obtain results for*random*regular*graphs*that match the main results for G(n, p). ... The classic Zero-One law for*random**graphs*states that if p is some constant*probability*then for every first order property the limiting*probability**of*the binomial*random**graph*G(n, p) having this property ... As mentioned earlier, the first order behavior*of**very**sparse**random*regular*graphs*was studied by Lynch [30] . ...##
###
The Consciousness Prior
[article]

2019
*
arXiv
*
pre-print

A low-dimensional thought or conscious state is analogous to a

arXiv:1709.08568v2
fatcat:35epysgf6rhe5c6kz6sgehvqli
*sentence*: it involves only a few variables and yet can make a statement with*very*high*probability**of*being true. ... This is consistent with a joint distribution (over high-level concepts) which has the form*of*a*sparse*factor*graph*, i.e., where the dependencies captured by each factor*of*the factor*graph*involve only ... Acknowledgements The author wants to thank Philippe Beaudoin, Gerry (Tong) Che, William Fedus, Devon Hjelm and Anirudh Goyal for preliminary discussions*about*the consciousness prior, as well as funding ...##
###
Patterns in syntactic dependency networks

2004
*
Physical Review E
*

., the ability

doi:10.1103/physreve.69.051915
pmid:15244855
fatcat:vxz4godpmnbklg5rc46hwpgodu
*of*combining words for forming*sentences*. The origin*of*such traits is an issue*of*open debate. ... Such previously unreported features*of*syntax organization are not a trivial consequence*of*the structure*of**sentences*, but an emergent trait at the global scale. ... The proportion*of*links provided with regard to the theoretical maximum is*about*0.16. The German corpus is the most*sparse**of*them. ...##
###
Enriching PubMed related article search with sentence level co-citations

2009
*
AMIA Annual Symposium Proceedings
*

In this paper, we propose to enrich PubMed with a new type

pmid:20351935
pmcid:PMC2815371
fatcat:cvtvofi7azanhhyw64jgvgtnfi
*of*related article link based on citations within a single*sentence*(i.e.*sentence*level co-citations or SLCs). ... We also showed that only half*of*SLCs are found among PubMed related article links. Additionally, we discuss how the citing*sentence**of*an SLC explains the connection between two articles. ... The PLCs extracted from the above mentioned 100,000 articles would form a*very**sparse*citation*graph*. ...##
###
HARPY: Hypernyms and Alignment of Relational Paraphrases

2014
*
International Conference on Computational Linguistics
*

To this end, we devise judicious features and develop a

dblp:conf/coling/GrycnerW14
fatcat:rs7s3yg2brdqfilcv2jxk3hzky
*graph*-based alignment algorithm by adapting and extending the SimRank*random*-walk method. ... The resulting taxonomy*of*relational phrases and verb senses, coined HARPY, contains 20,812 synsets organized into a Directed Acyclic*Graph*(DAG) with 616,792 hypernymy links. ... However, the subsumption hierarchy*of*Patty is*very**sparse*. ...##
###
Cognitive agents and machine learning by example: Representation with conceptual graphs

2018
*
Computational intelligence
*

The average length

doi:10.1111/coin.12167
fatcat:fdi3fqgxivfsbgyusdtlmghu5q
*of*a*sentence*increases from set to set, from 5 words per input up to 30 words per input. Figure 5 (*Graph*Example) shows an example*of*a simple CG. [ Figure 5*about*here.] ... Continuous updating*of*the*probabilities*look-up table enabled us to create a*very*large training sample set for the ANNs. ...##
###
Small Worlds of Concepts and Other Principles of Semantic Search
[chapter]

2003
*
Lecture Notes in Computer Science
*

A combination

doi:10.1007/978-3-540-39884-4_2
fatcat:z57m6u7chnfihb47qepi54lqs4
*of*the strengths*of*both classic information retrieval with the distributed approach*of*P2P networks can avoid both their weaknesses: The organisation*of*document collections relevant for ... On the other hand, the clustering coefficient will always be*very*low for the*random**graph*as opposed to the regular*graph*. ... If*graphs*are*sparse**graphs*, then with growing*graph*size the path length will grow linearly for the regular*graph*and only logarithmically for the*random**graph*because*of*the many possible shortcuts throughout ...##
###
Zero-one laws for k-variable first-order logic of sparse random graphs
[article]

2019
*
arXiv
*
pre-print

In this paper, we prove that for every positive ε, there exists an α∈(1/(k-1),1/(k-1)+ε) such that the binomial

arXiv:1811.07026v2
fatcat:2sa3ztzqgra6xmoquaefr4f63i
*random**graph*G(n,n^-α) does not obey 0-1 law w.r.t. first order*sentences*with k variables ... In this paper, we study asymptotical behavior*of**probabilities**of*truth*of*FO*sentences*from L k on the binomial*random**graph*G(n, p). ... For α > 2, the*random**graph*obeys the zero-one law w.r.t. L ω ∞,ω since a.a.s. (with asymptotical*probability*1) this*graph*is empty. ...##
###
Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings
[article]

2018
*
arXiv
*
pre-print

Word embeddings aims to map sense

arXiv:1808.05907v1
fatcat:5yu2t6v4uvbbjfmxixefi4k67u
*of*the words into a lower dimensional vector space in order to reason over them. ... We propose a*graph*based embedding algorithm inspired from node2vec. Experimental results have shown that our algorithm improves the syntactic strength and gives robust performance on meagre data. ... This method in a way inhibits the movement*of**random*walk from leaf nodes since there are less nodes connected to it, that lie at the end*of*parse trees, the tag transition matrix for them is*sparse*. ...##
###
Convergence law for hyper-graphs with prescribed degree sequences
[article]

2015
*
arXiv
*
pre-print

It defines a

arXiv:1501.07429v3
fatcat:bekcrtkkbrfanjgxwdzqk5b2vy
*random*hyper-multigraph specified by two distributions, one for the degrees*of*the vertices, and one for the sizes*of*the hyper-edges. ... We view hyper-*graphs*as incidence*graphs*, i.e. bipartite*graphs*with a set*of*nodes representing vertices and a set*of*nodes representing hyper-edges, with two nodes being adjacent if the corresponding ... Most natural*graphs*have a*sparse*structure yet are locally highly clustered, a property that classical*random**graphs*fail to model. ...##
###
Randomisation and Derandomisation in Descriptive Complexity Theory

2011
*
Logical Methods in Computer Science
*

We study probabilistic complexity classes and questions

doi:10.2168/lmcs-7(3:14)2011
fatcat:ez6mz5hh3fcoda6ka726764rre
*of*derandomisation from a logical point*of*view. ... The latter*of*these queries shows that certain uniform variants*of*AC0 (bounded-depth polynomial sized circuits) cannot be derandomised. ... Acknowledgements We would like to thank Nicole Schweikardt and Dieter van Melkebeek for helpful comments on an earlier version*of*this paper. ...##
###
Regular decomposition of large graphs and other structures: scalability and robustness towards missing data
[article]

2017
*
arXiv
*
pre-print

In this way it would be possible to find out a large scale structure

arXiv:1711.08629v1
fatcat:foj52rbyu5g3jflxk2i6krb5gi
*of*a huge*graphs**of*certain type using only a tiny part*of**graph*information and obtaining a compact representation*of*such*graphs*useful ... We continue our previous work on the subject, considering cases*of*missing data and scaling*of*algorithms to extremely large size*of**graphs*. ... The work*of*the Finnish authors was supported by Academy*of*Finland project 294763 (Stomograph). ...
« Previous

*Showing results 1 — 15 out of 9,478 results*