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Streaming graph partitioning for large distributed graphs

Isabelle Stanton, Gabriel Kliot
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
Existing graph partitioning heuristics incur high computation and communication cost on large graphs, sometimes as high as the future computation itself.  ...  The heuristics are scalable in the size of the graphs and the number of partitions.  ...  On some graphs, with some orderings, a variety of heuristics obtain results which are very close to the offline METIS result.  ... 
doi:10.1145/2339530.2339722 dblp:conf/kdd/StantonK12 fatcat:nqaphisol5bozfusje4zxuqdze

Graph-based data mining

D.J. Cook, L.B. Holder
2000 IEEE Intelligent Systems and their Applications  
− − − ( ) − = ∑ There are many possible reasons that some proteins did not show a pattern.  ...  Figure 5 graphs the runtime of distributed Subdue on two classes of graphs as the number of processors increases.  ... 
doi:10.1109/5254.850825 fatcat:uhmbej7osncgndxkc7rbyvvtmi

Attack Graph Obfuscation [article]

Rami Puzis, Hadar Polad, Bracha Shapira
2019 arXiv   pre-print
We use the attack graphs to model the path of an attacker making its way towards a target in a given network.  ...  We investigate the effect of fake vulnerabilities within a real enterprise network on the attacker performance.  ...  One of the reoccurring simplifying assumption is the knowledge of the network connectivity [32] .  ... 
arXiv:1903.02601v1 fatcat:lmypvi376vb2lfu6b5r4xdtkxy

Evolving Twitter: an experimental analysis of graph properties of the social graph [article]

Despoina Antonakaki, Sotiris Ioannidis, Paraskevi Fragopoulou
2015 arXiv   pre-print
Twitter is one of the most prominent Online Social Networks. It covers a significant part of the online worldwide population~20% and has impressive growth rates.  ...  The social graph of Twitter has been the subject of numerous studies since it can reveal the intrinsic properties of large and complex online communities.  ...  Acknowledgements We would like to thank Marian Boguna and Kolja Kleineberg on the discussions and contribution on the infrastructure on the University of Barcelona.  ... 
arXiv:1510.01091v1 fatcat:7egy63dzbbc3paht745uucsg6i

Coloring Big Graphs with AlphaGoZero [article]

Jiayi Huang, Mostofa Patwary, Gregory Diamos
2019 arXiv   pre-print
As a result, we are able to learn new state of the art heuristics for graph coloring.  ...  Key to our approach is the introduction of a novel deep neural network architecture (FastColorNet) that has access to the full graph context and requires $O(V)$ time and space to color a graph with $V$  ...  Furthermore, given the computational cost of training deep neural networks, it is only feasible to train on about 1 billion moves in a reasonable amount of time, even on high performance clusters of accelerators  ... 
arXiv:1902.10162v3 fatcat:vr625w5fgbf4loln2za3nb4hqm

Robustification of Online Graph Exploration Methods [article]

Franziska Eberle, Alexander Lindermayr, Nicole Megow, Lukas Nölke, Jens Schlöter
2021 arXiv   pre-print
We initiate the study of a learning-augmented variant of the classical, notoriously hard online graph exploration problem by adding access to machine-learned predictions.  ...  We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm and significantly outperforms any known online algorithm if the prediction is of high accuracy  ...  The assumption of having no prior knowledge about the graph may be overly pessimistic.  ... 
arXiv:2112.05422v1 fatcat:4vj243qg6jfsli6asq2w6xfit4

Knowledge Graphs [article]

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo (+6 others)
2021 arXiv   pre-print
We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques.  ...  We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs.  ...  Acknowledgements: We thank the attendees of the Dagstuhl Seminar on "Knowledge Graphs" for discussions that inspired and influenced this paper, and all those that make such seminars possible.  ... 
arXiv:2003.02320v5 fatcat:ab4hmm2f2bbpvobwkjw4xbrz4u

Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks [article]

Salman Mohammed, Peng Shi, Jimmy Lin
2018 arXiv   pre-print
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact.  ...  On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also  ...  Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.  ... 
arXiv:1712.01969v2 fatcat:frf4chbaxndyfeq65eumyp5wy4

Graph-Based Continual Learning [article]

Binh Tang, David S. Matteson
2021 arXiv   pre-print
Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an array of independent memory slots.  ...  Empirical results on several benchmark datasets show that our model consistently outperforms recently proposed baselines for task-free continual learning.  ...  There are several reasons for making the graphs G and A random.  ... 
arXiv:2007.04813v2 fatcat:gk7hyy5plfgijdjhr7cjfs3sga

xDGP: A Dynamic Graph Processing System with Adaptive Partitioning [article]

Luis Vaquero, Felix Cuadrado, Dionysios Logothetis, Claudio Martella
2013 arXiv   pre-print
This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication.  ...  Many real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges.  ...  The authors would like to thank Álvaro Navas and Steve Uhlig for their continuous support and advice during the elaboration of this work.  ... 
arXiv:1309.1049v3 fatcat:ddj22wkbozdlln5cewcwusnm5i

Combinatorial optimization and reasoning with graph neural networks [article]

Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković
2021 arXiv   pre-print
Until recently, its methods have focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice.  ...  The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity.  ...  Some of the learnable parameters are responsible for generating feasible solutions, while others focus on minimizing the solution cost.  ... 
arXiv:2102.09544v2 fatcat:eweej3mq2bbohaifazeghswcpi

Managing large dynamic graphs efficiently

Jayanta Mondal, Amol Deshpande
2012 Proceedings of the 2012 international conference on Management of Data - SIGMOD '12  
Second, we propose a clustering-based approach to amortize the costs of making these replication decisions.  ...  However, although there is much work on singlesite graph databases and on efficiently executing different types of queries over large graphs, to date there is little work on understanding the challenges  ...  [40, 41] considered one extreme version of it for scaling online social networks: they aim to replicate the graph sufficiently so that, for every node in the graph, all of its neighbors are present  ... 
doi:10.1145/2213836.2213854 dblp:conf/sigmod/MondalD12 fatcat:mzeru22b6ff3pj34oefuehzpa4

Domain-specific Knowledge Graphs: A survey [article]

Bilal Abu-Salih
2021 arXiv   pre-print
This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine.  ...  Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation.  ...  Authors of [145] depicted the relevance of using a knowledge-empowered model on event representation and stock prediction. Zhang et al.  ... 
arXiv:2011.00235v3 fatcat:oc2loewqdjfgvlapy4kmult5li

Neural Online Graph Exploration [article]

Ioannis Chiotellis, Daniel Cremers
2021 arXiv   pre-print
To the best of our knowledge, this is the first attempt to solve online graph exploration in a data-driven way.  ...  To answer this question, we study the problem of Online Graph Exploration, the online version of the Traveling Salesperson Problem.  ...  The goal of the agent is to visit all nodes in the graph, while paying the minimum cost.  ... 
arXiv:2012.03345v2 fatcat:7czvjtzgrndz3oses7sqfq2yta

Graph mining

Deepayan Chakrabarti, Christos Faloutsos
2006 ACM Computing Surveys  
Further, we briefly describe recent advances on some related and interesting graph problems.  ...  This survey give an overview of the incredible variety of work that has been done on these problems.  ...  We will first discuss how a good-for-navigation graph can be designed and then briefly touch upon some work investigating the reasons behind the ease of navigation on the WWW.  ... 
doi:10.1145/1132952.1132954 fatcat:jbc67scdzbe4vj5o5fjerdy6xq
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