Graph Mining and Social Networks
Curated by: Christos Faloutsos
Have you ever wondered how Google finds the best page for your question? How would you spot the most important people on faceBook? How would you spot fake followers on Twitter? In a who-contacts- whom network, which is the best nodes to immunize, to stop a flu epidemic?
All these problems, and myriad more, use “graph mining” methods. But graph mining is not restricted to social networks: in computer-to- computer communication networks we want to find whether a computer is under cyber-attack (and protect it, before-hand); in a user-product review system, we want to find fake reviews; in a prey-predator ecological system, we want to find the most important species, to protect the system from unraveling.
Graph mining uses sophisticated mathematical methods (“linear algebra”, “eigenvalue analysis”, “matrix factorizations”, “tensors”), which pay off spectacularly - Google’s PageRank algorithm being the most obvious example.
Link: http://www.cs.cmu.edu/~christos/TALKS/16-graph-mining-intro-kdd/
For K-12
- Ever wondered how the characters of 'Les Miserables' relate to each other? If yes, check this interactive animation!
- Do you have a facebook account? do you want to know how the people are connected, over the globe? facebook image
For CS professionals
- Do you want to learn more about graph analytics? Check this book (free, from most libraries and *.edu accounts, in the US)
- Do you want to try your graph analytics algorithms, on real data? Check these wonderful collections
Related KDD2016 Papers
Title & Authors |
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Robust Influence Maximization Author(s): Xinran He*, University of Southern California; David Kempe, University of Southern California |
How to Compete Online for News Audience: Modeling Words that Attract Clicks Author(s): Joon Hee Kim*, KAIST; Amin Mantrach, Yahoo! Research; Alex Jaimes, Yahoo!; Alice Oh, Korea Advanced Institute of Science and Technology |
Approximate Personalized PageRank on Dynamic Graphs Author(s): Hongyang Zhang*, Stanford University; Peter Lofgren, Stanford University |
Compact and Scalable Graph Neighborhood Sketching Author(s): Takuya Akiba*, NII; Yosuke Yano, National Institute of Informatics |
Structural Neighborhood based Classification of Nodes in a Network Author(s): Sharad Nandanwar*, Indian Institute of Science; Musti Narasimha Murty, Indian Institute of Science |
User Identity Linkage by Latent User Space Modelling Author(s): Xin Mu*, Nanjing University; Feida Zhu, Singapore Management Univ.; Zhi-Hua Zhou, ; Ee-Peng Lim, Singapore Management University; Jing Xiao, ; Jianzong Wang, |
Kam1n0: MapReduce-based Assembly Clone Search for Reverse Engineering Author(s): Steven H. H. Ding, McGill University; Benjamin C. M. Fung*, McGill University; Philippe Charland, Defence Research and Development Canada |
Joint Community and Structural Hole Spanner Detection via Harmonic Modularity Author(s): Lifang He*, ; CHUN-TA LU, UIC; Jiaqi Ma, Tsinghua University; Jianping Cao, NUDT; Linlin Shen, ; Philip S. Yu, UI Chicago |
Burstiness Scale: a highly parsimonious model forcharacterizing random series of events Author(s): Rodrigo Alves*, CEFET-MG; Renato Assunção, DCC-UFMG; Pedro O.S. Vaz de Melo, DCC-UFMG |
Talent Circle Detection in Job Transition Networks Author(s): Huang Xu*, Northwestern Polytechnical Uni; Jingyuan Yang, Rutgers University; zhi wen Yu, ; Hui Xiong, Rutgers; Hengshu Zhu, Baidu Inc. |
Sampling of Attributed Networks From Hierarchical Generative Models Author(s): Pablo Robles Granda*, Purdue University; Sebastian Moreno, ; Jennifer Neville, Purdue |
FINAL: Fast Attributed Network Alignment Author(s): Si Zhang*, Arizona State University; Hanghang Tong, Arizona State University |
FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks Author(s): Chen Chen*, Arizona State Unversity; Hanghang Tong, Arizona State University; Lei Xie, City University of New York; Lei YIng, Arizona State University; Qing He, Arizona State University |
Finding Gangs in War from Signed Networks Author(s): Lingyang Chu*, Simon Fraser University; Zhefeng Wang, University of Science and Technology of China; Jian Pei, Simon Fraser University; Jiannan Wang, Simon Fraser University; Zijin Zhao, Simon Fraser University; Enhong Chen, |
Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platfo Author(s): Alexander Nikolaev*, University at Buffalo; Shounak Gore, University at Buffalo; Venu Govindaraju, University at Buffalo |
QUINT: On Query-Specific Optimal Networks Author(s): Liangyue Li*, Arizona State University; Yuan Yao, Nanjing University; Jie Tang, Tsinghua University; Wei Fan, Baidu; Hanghang Tong, Arizona State University |
Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay Author(s): Yu Shi*, UIUC; Myunghwan Kim, LinkedIn Corporation; Shaunak Chatterjee, LinkedIn Corporation; Mitul Tiwari, LinkedIn Corporation; Souvik Ghosh, LinkedIn; Romer Rosales, LinkedIn |
Ranking Universities Based on Career Outcomes of Graduates Author(s): Navneet Kapur (GoFundMe), Nikita Lytkin (LinkedIn Corporation), Bee-Chung Chen (LinkedIn Corporation), Deepak Agarwal (LinkedIn Corporation), Igor Perisic (LinkedIn Corporation) |
Come-and-Go Patterns of Group Evolution: A Dynamic Model Author(s): Tianyang Zhang*, Tsinghua University; Peng Cui, Tsinghua University; Christos Faloutsos, Carnegie Mellon University; Wenwu Zhu, Tsinghua University; Shiqiang Yang, |
node2vec: Scalable Feature Learning for Networks Author(s): Aditya Grover*, Stanford University; Jure Leskovec, Stanford University |
Meta Structure: Computing Relevance in Large Heterogeneous Information Networks Author(s): Zhipeng Huang*, University of Hong Kong; Yudian Zheng, The University of Hong Kong; Reynold Cheng, ; Yizhou Sun, Northeastern Univ; Nikos Mamoulis, ; Xiang Li, The University of Hong Kong |
Reconstructing an Epidemic over Time Author(s): Polina Rozenshtein, Aalto University; Aristides Gionis*, Aalto University; B. Aditya Prakash, Virginia Tech; Jilles Vreeken, Max-Planck Institute for Informatics and Saarland University |
Identifying Decision Makers from Professional Social Networks Author(s): Shipeng Yu*, LinkedIn; Evangelia Christakopoulou, University of Minnesota; Abhishek Gupta, LinkedIn |
A Truth Discovery Approach with Theoretical Guarantee Author(s): Houping Xiao*, SUNY Buffalo; Jing Gao, ; Zhaoran Wang, Princeton University; Shiyu Wang, UIUC; Lu Su, SUNY Buffalo; Han Liu, Princeton University |
Smart broadcasting: Do you want to be seen? Author(s): Erfan Tavakoli, Sharif University; Mohammad Reza Karimi, Sharif University; Mehrdad Farajtabar, Georgia Tech; Le Song, ; Manuel Gomez-Rodriguez*, MPI-SWS |
When Social Influence Meets Item Inference Author(s): Hui-Ju Hung, Pennsylvania State University; Hong-Han Shuai, Academia Sinica; De-Nian Yang*, Academic Sinica; Liang-Hao Huang, Academia Sinica; Wang-Chien Lee, The Pennsylvania State University; Jian Pei, Simon Fraser University; Ming-Syan Chen, National Taiwan University |
A multiple test correction for streams and cascades of statistical hypothesis tests Author(s): Geoff Webb*, Monash University; Francois Petitjean, Monash |
Effcient Processing of Network Proximity Queries via Chebyshev Acceleration Author(s): Mustafa Coskun*, Case Western University; Ananth Grama, ; Mehmet Koyuturk, |
Robust Influence Maximization Author(s): Wei Chen, Microsoft Research; Tian Lin*, Tsinghua University; Zihan Tan, IIIS, Tsinghua University; Mingfei Zhao, IIIS, Tsinghua University; Xuren Zhou, The Hong Kong University of Science and Technology |
Graph Wavelets via Sparse Cuts Author(s): Arlei Lopes da Silva*, UC, Santa Barbara; Xuan-Hong Dang, UCSB; Prithwish Basu, Raytheon BBN; Ambuj Singh, UCSB; Ananthram Swami, Army Lab |
Diversified Temporal Subgraph Pattern Mining Author(s): Yi Yang, Fudan University; Da Yan, CUHK; Huanhuan Wu, CUHK; James Cheng*, CUHK; Shuigeng Zhou, Fudan University; John C.S. Lui, The Chinese University of Hong Kong |
CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors Author(s): Meng Jiang*, UIUC; Christos Faloutsos, Carnegie Mellon University; Jiawei Han, University of Illinois at Urbana-Champaign |
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs Author(s): Emaad Manzoor, Stony Brook University; Leman Akoglu*, SUNY Stony Brook |
Asymmetric Transitivity Preserving Graph Embedding Author(s): Mingdong Ou*, Tsinghua University; Peng Cui, Tsinghua University; Jian Pei, Simon Fraser University; Wenwu Zhu, Tsinghua University |
The Limits of Popularity-Based Recommendations, and the Role of Social Ties Author(s): Marco Bressan*, Sapienza University of Rome; Stefano Leucci, Sapienza University of Rome; Alessandro Panconesi, Sapienza University of Rome; Prabhakar Raghavan, Google; Erisa Terolli, Sapienza University of Rome |
Structural Deep Network Embedding Author(s): DAIXIN WANG*, TSINGHUA UNIVERSITY; Peng Cui, Tsinghua University; Wenwu Zhu, Tsinghua University |
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages Author(s): Matteo Riondato*, Two Sigma Investments; Eli Upfal, Brown University |
PTE: Enumerating Trillion Triangles On Distributed Systems Author(s): Ha-Myung Park*, KAIST; Sung-Hyon Myaeng, KAIST; U Kang, Seoul National University |
Scalable Betweenness Centrality Maximization via Sampling Author(s): Ahmad Mahmoody*, Brown University; Eli Upfal, Brown University; Charalampos Tsourakakis, Harvard |
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations Author(s): Wei Cheng*, NEC Labs America; Kai Zhang, NEC labs America; Haifeng Chen, NEC Research Lab; Guofei Jiang, NEC labs America; Wei Wang, UC Los Angeles |
Compressing Graphs and Indexes with Recursive Graph Bisection Author(s): Laxman Dhulipala, Carnegie Mellon University; Igor Kabiljo, Facebook; Brian Karrer, Facebook; Giuseppe Ottaviano, Facebook; Sergey Pupyrev*, Facebook; Alon Shalita, Facebook |
Positive-Unlabeled Learning in Streaming Networks Author(s): Shiyu Chang*, UIUC; Yang Zhang, UIUC; Jiliang Tang, Yahoo Labs; Dawei Yin, ; Yi Chang, Yahoo! Labs; Mark Hasegawa-Johnson, UIUC; Thomas Huang, UIUC |
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage Author(s): Bryan Hooi*, Carnegie Mellon University; Hyun Ah Song, Carnegie Mellon University; Alex Beutel, Carnegie Mellon University; Neil Shah, Carnegie Mellon University; Kijung Shin, Carnegie Mellon University; Christos Faloutsos, Carnegie Mellon University |
Inferring Network Effects from Observational Data Author(s): David Arbour*, University of Massachusetts Am; Dan Garant, University of Massachusetts Amherst; David Jensen, UMass Amherst |
TRIEST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size Author(s): Lorenzo De Stefani*, Brown University; Alessandro Epasto, Brown; Matteo Riondato, Two Sigma Investments; Eli Upfal, Brown University |