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Jerzy Jaworski

dblp:63/528 · DBLP profile ↗
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5ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0003-4520-6451ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 4 · 1 first-author · 1 since 2021Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory › random walk
cover time
0.612022
The Cover Time of a Random Walk in Affiliation Networks · IEEE Trans. Inf. Theory 2022
Graph algorithms and graph theory
random graphs
0.612022
The Cover Time of a Random Walk in Affiliation Networks · IEEE Trans. Inf. Theory 2022
Graph algorithms and graph theory › random graphs
random intersection graphs
0.612022
The Cover Time of a Random Walk in Affiliation Networks · IEEE Trans. Inf. Theory 2022
Graph algorithms and graph theory
random walk
0.612022
The Cover Time of a Random Walk in Affiliation Networks · IEEE Trans. Inf. Theory 2022

Methods — techniques the papers use, named apart from their topics

probabilistic analysis · 1.1
YearPublicationVenuePosition
2022 The Cover Time of a Random Walk in Affiliation Networks
abstract
Many known networks have structure of affiliation networks, where each of$n$network nodes (actors) selects an attribute set from a given collection of$m$attributes, and two nodes (actors) establish adjacency relation whenever they share a common attribute. We study the behavior of a random walk on such networks. For this purpose, we use a common model of such networks, a random intersection graph. We establish the cover time of the simple random walk on the binomial random intersection graph${\mathcal{ G}}(n,m,p)$at the connectivity threshold and above it. We consider the range of$(n,m,p)$, where the typical attribute is shared by a (stochastically) bounded number of actors.
Mindaugas Bloznelis, Jerzy Jaworski, Katarzyna Rybarczyk
IEEE Trans. Inf. Theory2
2020 A Note on the Conductance of the Binomial Random Intersection Graph
Katarzyna Rybarczyk, Mindaugas Bloznelis, Jerzy Jaworski
WAW3
2018 The Asymptotic Normality of the Global Clustering Coefficient in Sparse Random Intersection Graphs
Mindaugas Bloznelis, Jerzy Jaworski
WAW2
2012 Preface
abstract
The Central European Conferences on Cryptology (CECC) were inaugurated in 2000 at the 14th Czech and Slovak International Conference on Number Theory in Liptovský Ján, Slovak Republic with a special crypto session.Since 2001 the CECC have been organized every year in a selected Central European country: in the Slovak Republic (three times), in Hungary and the Czech Republic (twice each), and in Poland and Austria (once each).The jubilee 10th CECC was held in Poland in 2010.The conference was organized by the Stefan Banach
Jerzy Jaworski, Mieczyslaw Kula, Damian Niwinski, Jerzy Urbanowicz
Fundam. Informaticae1
2009 Component evolution in a secure wireless sensor network
abstract
Abstract We study a connectivity property of a secure wireless network that uses random pre‐distribution of keys. A network is composed of n sensors. Each sensor is assigned a collection of d different keys drawn uniformly at random from a given set of m keys. Two sensors are joined by a communication link if they share a common key. We show that for large n with high probability the connected component of size Ω( n ) emerges in the network when the probability of a link exceeds the threshold 1/ n . Similar component evolution is shown for networks where sensors communicate if they share at least s common keys. © 2008 Wiley Periodicals, Inc. NETWORKS, 2009
Mindaugas Bloznelis, Jerzy Jaworski, Katarzyna Rybarczyk
Networks2