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Big Graph Analytics Platforms

Da Yan, Yingyi Bu, Yuanyuan Tian, Amol Deshpande
2017 Foundations and Trends in Databases  
big graph analytics.  ...  Features of Big Graph Systems We can categorize the big graph platforms along various dimensions.  ...  We only present the simple case where v in and v out fit in main memory, and thus the computation only needs to read adjacency lists (i.e., columns of A T ) from SSD.  ... 
doi:10.1561/1900000056 fatcat:ucqrtzo4q5g2lpj6dmp7jv3e5m

Querying big graphs within bounded resources

Wenfei Fan, Xin Wang, Yinghui Wu
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
Given a query Q, a graph G and a small ratio α, it aims to answer Q in G by accessing only a fraction GQ of G of size |GQ| ≤ α|G|.  ...  We find that they scale well for both types of queries, and our approximate answers are accurate, even 100% for small α.  ...  EP/J015377/1 and NSF III 1302212.  ... 
doi:10.1145/2588555.2610513 dblp:conf/sigmod/FanWW14 fatcat:zwjextk4sngnnjspvttgrtc5rm

Big graph mining for the web and social media

U. Kang, Leman Akoglu, Duen Horng Chau
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
We start with important graph algorithms that are central to graph mining and pattern discoveries, including graphbased anomaly detection techniques (complement of pattern discoveries) that are playing  ...  And what kind of real-world problems, associated with the Web and social media, can we solve with such tools? These are exactly the goals of this tutorial.  ...  His research interests include data mining in big graphs. He leads the research on award-winning Pegasus [20] . Leman Akoglu is an assistant professor at Stony Brook University.  ... 
doi:10.1145/2556195.2556198 dblp:conf/wsdm/KangAC14 fatcat:fbe7ciirlzd3xm42h3y67bw77i

Drawing Big Graphs using Spectral Sparsification [article]

Peter Eades, Quan Nguyen, Seok-Hee Hong
2017 arXiv   pre-print
We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs.  ...  Our results lead to guidelines for using spectral sparsification in big graph visualization.  ...  beyond a few hundred nodes [22] .  ... 
arXiv:1708.08659v2 fatcat:kd4rzq3dlzeavbk4pmixhkexjq

Drawing Big Graphs Using Spectral Sparsification [chapter]

Peter Eades, Quan Nguyen, Seok-Hee Hong
2018 Lecture Notes in Computer Science  
We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs.  ...  Our results lead to guidelines for using spectral sparsification in big graph visualization.  ...  beyond a few hundred nodes [22] .  ... 
doi:10.1007/978-3-319-73915-1_22 fatcat:rp7dtzl4hjczbnhlgt5ojsmgje

Exploration and Visualization of Big Graphs - The DBpedia Case Study

Enrico G. Caldarola, Antonio Picariello, Antonio M. Rinaldi, Marco Sacco
2016 Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
In this paper, we take one of this emerging tools, namely Neo4J, and stress its capabilities in order to import, query and visualize data coming from a big case study: DBpedia.  ...  Increasingly, the data and information visualization is becoming strategic for the exploration and explanation of large data sets.  ...  By extracting the structured content of Wikipedia, such as infoboxes, tables, lists, and categorization data, and putting them in a consistent knowledge base, it is possible to answer expressive queries  ... 
doi:10.5220/0006046802570264 dblp:conf/ic3k/CaldarolaPRS16 fatcat:j6unoif3f5abtpoar7wv5eehhq

Big Graph Analyses: From Queries to Dependencies and Association Rules

Wenfei Fan, Chunming Hu
2017 Data Science and Engineering  
We also identify open problems in connection with querying, cleaning and mining big graphs.  ...  Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks.  ...  reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s41019-016-0025-x fatcat:xrvufreijrhuzc77kaiancubxm

Big Graph Search: Challenges and Techniques [article]

Shuai Ma, Jia Li, Chunming Hu, Xuelian Lin, Jinpeng Huai
2014 pre-print
Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.  ...  After that, we analyze the difficulties and challenges of big graph search.  ...  This work is supported in part by 973 program (No. 2014CB340300), NSFC (No. 61322207) and the Fundamental Research Funds for the Central Universities.  ... 
doi:10.1007/s11704-015-4515-1. arXiv:1411.4266v1 fatcat:okwftz4wovb3viq3mxbfgujvsu

The Future is Big Graphs! A Community View on Graph Processing Systems [article]

Sherif Sakr, Angela Bonifati, Hannes Voigt, Alexandru Iosup, Khaled Ammar, Renzo Angles, Walid Aref, Marcelo Arenas, Maciej Besta, Peter A. Boncz, Khuzaima Daudjee, Emanuele Della Valle (+29 others)
2020 pre-print
What needs to happen in the next decade for big graph processing to continue to succeed?  ...  Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems.  ...  In the meantime, join us in solving the problems of big graph processing. The future is big graphs!  ... 
doi:10.1145/3434642 arXiv:2012.06171v1 fatcat:gyuchitjqjedpmlnxdvunjyzba

Big Graph : Tools, Techniques, Issues, Challenges and Future Directions

Dhananjay Kumar Singh, Ripon Patgiri
2016 Computer Science & Information Technology ( CS & IT )   unpublished
In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations.  ...  protein networks, transportation networks, bibliographical networks, knowledge bases and many more).  ...  BIG GRAPH We can simply define Big Graph as, "Big Data + Structure = Big Graph" Big graphs are ubiquitous, ranging from social networks and mobile call networks to biological networks and the World Wide  ... 
doi:10.5121/csit.2016.60911 fatcat:jl2lduqe5nbuxgfmjg4mkidh7e

Graph Databases: Their Power and Limitations [chapter]

Jaroslav Pokorný
2015 Lecture Notes in Computer Science  
The ideal graph database should understand analytic queries that go beyond k-hop queries for small k.  ...  In Big Graphs often approximate matching is needed. Allowing structural relaxation, then we talk about structural similarity queries.  ... 
doi:10.1007/978-3-319-24369-6_5 fatcat:rdfl2pczh5fkhea7bdib52us2a

Mining big data

Wei Fan, Albert Bifet
2013 SIGKDD Explorations  
We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.  ...  Big Data is a new term used to identify the datasets that due to their large size and complexity, we can not manage them with our current methodologies or data mining software tools.  ...  ACKNOWLEDGEMENTS We would like to thank Jimmy Lin, Dmitriy Ryaboy, Jiawei Han, Yizhou Sun, U Kang, Christos Faloutsos and Xavier Amatriain for contributing to this special section.  ... 
doi:10.1145/2481244.2481246 fatcat:4desfvwbqrfhpgez4cm7wwxwtu

NetworkRepository: An Interactive Data Repository with Multi-scale Visual Analytics [article]

Ryan A. Rossi, Nesreen K. Ahmed
2015 arXiv   pre-print
., UCI ML Data Repository, and SNAP), the network data repository ( allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive  ...  Users can in real-time analyze, visualize, compare, and explore data along many different dimensions.  ...  Acknowledgments We thank all of the donors who contributed data to the repository and all others who have supported and continue to support this effort.  ... 
arXiv:1410.3560v2 fatcat:lm7ejqowdrbm3huj6ycd562rxq

Towards Balance-Affinity Tradeoff in Concurrent Subgraph Traversals

Yinglong Xia, Lifeng Nai, Jui-Hsin Lai
2015 2015 IEEE International Parallel and Distributed Processing Symposium  
In many cases, such queries result in local subgraph traversals, which essentially require an efficient scheduling scheme to explore the tradeoff between the workload balance and the task affinity.  ...  Since those systems are typically wrapped as service providers in industry, it is critical to handle concurrent queries at runtime by incorporating a set of parallel processing units.  ...  The tasks defined on such big graphs typically are variances of local subgraph traversals.  ... 
doi:10.1109/ipdps.2015.25 dblp:conf/ipps/XiaNL15 fatcat:ng62xgbuzfg6fode5jnzj5hzti

An Insight to Data Stream Mining and other Emerging Learning Algorithms

Shravan Vishwanathan, Thirunavukkarasu K
2014 IOSR Journal of Computer Engineering  
Significant advancements have been made in the field of data mining and knowledge discovery.  ...  We have tried to analyse the most effective and recent algorithms and techniques which have been developed to mine information from different data sources.  ...  U Kang (2012) [26] proposed a Big graph mining system Pegasus. The framework is built on top of MapReduce(Hadoop) to provide the edge of distributed processing.  ... 
doi:10.9790/0661-16321924 fatcat:ig24i2is25habliv2kxnht3oeq
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