LEADER 05723nam 22005053 450 001 996594166603316 005 20240503084511.0 010 $a3-031-59205-0 035 $a(CKB)31801763500041 035 $a(MiAaPQ)EBC31310591 035 $a(Au-PeEL)EBL31310591 035 $a(MiAaPQ)EBC31319746 035 $a(Au-PeEL)EBL31319746 035 $a(EXLCZ)9931801763500041 100 $a20240503d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModelling and Mining Networks $e19th International Workshop, WAW 2024, Warsaw, Poland, June 3-6, 2024, Proceedings 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing AG,$d2024. 210 4$d©2024. 215 $a1 online resource (194 pages) 225 1 $aLecture Notes in Computer Science Series ;$vv.14671 311 $a3-031-59204-2 327 $aIntro -- Preface -- Organization -- Contents -- Subgraph Counts in Random Clustering Graphs -- 1 Introduction -- 2 Preliminaries -- 2.1 Volumes -- 2.2 The Chung-Lu Model -- 2.3 Random Clustering Graphs -- 3 Extension Configurations -- 4 Subgraph Counts -- 5 Clustering Coefficient and Cycle Counts -- References -- Self-similarity of Communities of the ABCD Model -- 1 Introduction -- 2 The ABCD Model -- 2.1 Notation -- 2.2 The Configuration Model -- 2.3 Parameters of the ABCD Model -- 2.4 The ABCD Construction -- 2.5 A Known Result for ABCD -- 3 Main Result -- 4 Simulation Corner -- 4.1 The Coupling -- 4.2 Volumes of Communities -- 4.3 Self-loops and Multi-edges -- 5 Conclusion -- References -- A Simple Model of Influence: Details and Variants of Dynamics -- 1 Introduction -- 2 The Influencer Problem on the Cycle Cn -- 2.1 Results for the Cycle Cn -- 2.2 Analysis for the Cycle Cn -- 3 The Influencer Problem for Random Graphs G(n,p) -- 3.1 Results for G(n,p) When p=c/n -- 3.2 Analysis for Random Graphs G(n,p) -- 3.3 Random Edge -- 3.4 Basic Falling-Out Model -- 3.5 General Falling-Out Model -- 3.6 Formalizing the DE for w.h.p. Results -- 4 Conclusions and Further Work -- References -- Impact of Market Design and Trading Network Structure on Market Efficiency -- 1 Research Objective and Paper Structure -- 2 Definition of Research Problem and Its Motivation -- 3 Market Designs on Complete and Sparse Bipartite Graphs -- 3.1 Chamberlin's Higgling Market Vs Perfect Competition Model -- 3.2 Greedy Matching of Traders on Network -- 4 Simulation Results -- 4.1 Market Efficiency Drivers -- 4.2 Trade Participation Drivers -- 5 Conclusions and Further Research -- References -- Network Embedding Exploration Tool (NEExT) -- 1 Introduction -- 2 The Framework -- 2.1 Pre-processing -- 2.2 Vectorizing the Nodes -- 2.3 Embedding of the Graphs -- 3 Experiments. 327 $a3.1 Synthetic Graphs -- 3.2 Real-World Networks -- 4 Conclusion -- References -- Efficient Computation of K-Edge Connected Components: An Empirical Analysis -- 1 Introduction -- 2 Definitions -- 3 Related Work -- 4 Algorithms -- 4.1 Graph Decomposition Algorithm -- 4.2 Random Contraction Algorithm -- 4.3 Early Merging and Splitting -- 4.4 Local Cut Detection -- 5 Experiments -- 5.1 Small Graphs -- 5.2 Medium and Large Graphs -- 5.3 Evaluation of Optimization Techniques for RC -- 6 Discussion -- References -- The Directed Age-Dependent Random Connection Model with Arc Reciprocity -- 1 Motivation and Background -- 2 Model Introduction -- 2.1 The Directed Age-Dependent Random Connection Model -- 2.2 A Generative Model on Finite Domains and a Local Limit Procedure -- 3 Local Properties -- 3.1 Degree Distribution -- 3.2 Clustering -- 4 Directed Percolation -- References -- How to Cool a Graph -- 1 Introduction -- 2 Bounds on the Cooling Number -- 3 Isoperimetric Results and Grids -- 4 Cooling the ILT Model -- 5 Conclusion and Further Directions -- References -- Distributed Averaging for Accuracy Prediction in Networked Systems -- 1 Introduction -- 2 Background -- 2.1 Network Topology -- 2.2 Distributed Average -- 2.3 Gossip Algorithms -- 2.4 Convergence Rate and Accuracy -- 3 Proposed Approach -- 3.1 Problem Setup -- 3.2 Simulations -- 3.3 Local Graph Averages -- 3.4 Regression Models -- 3.5 Distributed Accuracy Prediction -- 4 Applications -- 4.1 Topology Changes -- 4.2 Anomaly Detection -- 5 Conclusion -- References -- Towards Graph Clustering for Distributed Computing Environments -- 1 Introduction -- 2 Model -- 3 Heuristic -- 4 Experiments -- 4.1 Performance on the Karate Graph -- 4.2 Performance on the ABCD Graph -- 4.3 Performance on a Road Network -- 5 Conclusions -- References. 327 $aHypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection -- 1 Introduction -- 2 Related Work -- 3 HypergraphRepository -- 3.1 Hypergraph Representations in HypergraphRepository -- 3.2 A Community-Driven Hypergraph Collection -- 3.3 An Interactive Hypergraph Repository -- 4 Conclusion -- References -- Clique Counts for Network Similarity -- 1 Introduction -- 2 Clique Profiles -- 3 Experimental Design and Methods -- 4 Discussion and Future Work -- References -- Author Index. 410 0$aLecture Notes in Computer Science Series 700 $aDewar$b Megan$01737394 701 $aKami?ski$b Bogumi?$01737395 701 $aKaszy?ski$b Daniel$01737396 701 $aKrai?ski$b ?ukasz$01737397 701 $aPra?at$b Pawe?$01737398 701 $aThéberge$b François$01737399 701 $aWrzosek$b Ma?gorzata$01737400 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996594166603316 996 $aModelling and Mining Networks$94159137 997 $aUNISA