Parallel Computing for Machine Learning in Social Network Analysis

Machine learning, especially deep learning, is revolutionizing how many engineering problems are being solved. Three critical ingredients are needed to apply deep machine learning to significant real world problems: i.) large data sets; ii.) software to implement deep learning and; iii.) significant computing cycles. This paper discusses the state of each ingredient with a specific focus on: a.) how deep learning can apply to large-scale social network analysis and; b.) the computing resources required to make such analyses feasible.

[1]  George Cybenko,et al.  Mining for Social Processes in Intelligence Data Streams , 2008 .

[2]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[3]  Alex Pentland,et al.  Social Ties as Predictors of Economic Development , 2016, NetSci-X.

[4]  Ted G. Lewis,et al.  Network Science: Theory and Applications , 2009 .

[5]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[6]  David J. King,et al.  An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents , 2016 .

[7]  George Cybenko,et al.  Dynamic Load Balancing for Distributed Memory Multiprocessors , 1989, J. Parallel Distributed Comput..

[8]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[9]  Eugene Santos,et al.  Culturally Infused Social Network Analysis , 2008, IC-AI.

[10]  George Cybenko,et al.  Identifying and tracking dynamic processes in social networks , 2006, SPIE Defense + Commercial Sensing.

[11]  Ji Wan,et al.  Deep Learning for Content-Based Image Retrieval: A Comprehensive Study , 2014, ACM Multimedia.

[12]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[13]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[14]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[15]  Michael I. Jordan,et al.  Real-Time Machine Learning: The Missing Pieces , 2017, HotOS.

[16]  Jennifer T. Chayes.  Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks , 2016, KDD.

[17]  Eugene Santos,et al.  Intent-Driven Behavioral Modeling during Cross-Border Epidemics , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[18]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[19]  Anupam Joshi,et al.  @i seek 'fb.me': identifying users across multiple online social networks , 2013, WWW.

[20]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[21]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[22]  Yun Liu,et al.  Modeling and predicting opinion formation with trust propagation in online social networks , 2017, Commun. Nonlinear Sci. Numer. Simul..

[23]  Li Dong,et al.  Predicting the attributes of social network users using a graph-based machine learning method , 2016, Comput. Commun..

[24]  D. Watts,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2001 .

[25]  B. Ribeiro,et al.  GPUMLib : An Efficient Open-Source GPU Machine Learning Library , 2011 .

[26]  Chong Wang,et al.  Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.

[27]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[28]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[29]  Razvan Pascanu,et al.  Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.

[30]  Dustin Arendt,et al.  An Effective Anytime Anywhere Parallel Approach for Centrality Measurements in Social Network Analysis , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[31]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .