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Representation Learning in Large Attributed Graphs

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Nesreen K. Ahmed
Nesreen K. Ahmed

Invited Talk at the Women in Machine Learning Workshop at NIPS 2017

Data & Analytics
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-­‐ -­‐ -­‐ -­‐ -­‐ Social  network   Human  Disease  Network   [Barabasi 2007] Food  Web  [2007] Terrorist  Network [Krebs  2002]Internet  (AS)  [2005] Gene  Regulatory  Network   [Decourty 2008] Protein  Interactions   [breast  cancer] Political  blogs Power  grid
input 0 … 1 … 0 … Feature   Engineering features 1 … 1 … 0 0 1 0 0 Learning   AlgorithmModel Prediction  Task Link  prediction Classification   Anomaly  detection
input 0 … 1 … 0 … Feature   Engineering features 1 … 1 … 0 0 1 0 0 Learning   AlgorithmModel Prediction  Task Automatic   Feature  Learning Link  prediction Classification   Anomaly  detection
§ Goal:  Learn  representation  (features)  for  a  set  of  graph   elements  (nodes,  edges,  etc.) § Key  intuition:  Map  the  graph  elements  (e.g.,  nodes)  to  the   d-­‐dimension  space,  while  preserving  node  similarity § Use  the  features  for  any  downstream  prediction  task
Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) DeepWalk Model Perrozi et  al.  KDD  2014
Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) How  to  get  nearby  nodes? Perrozi et  al.  KDD  2014 Grover  et  al.  KDD  2016
Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) § A  (conditional)  walk/path  is  a  finite  sequence  of  adjacent   vertices  in  the  graph How  to  get  nearby  nodes? Perrozi et  al.  KDD  2014 Grover  et  al.  KDD  2016
V1 V3 V4 V2 V5 The  random  walk  traversed  link  V1  -­‐-­‐-­‐ V2 Evaluating  next  step  at  node  V2
Mikolov et  al.  ICLR  2013 Perrozi et  al.  KDD  2014 focus  vertex
§ No  support  for  inductive/transfer  learning • features  are  learned  for  node  identities   • features  do  not  generalize  beyond  the  input  graph § Map  nodes  based  on  their  proximity  only § No  notion  of  attributes § No  notion  of  structural  similarity
Communities:  cohesive  subsets  of  nodes Roles:  represent  structural  patterns -­‐ two  nodes  belong  to  the  same  role  if  they’ve  similar  structural  patterns Cj# Ci# Ck# Rossi  &  Ahmed  TKDE  2015 Ahmed  et  al.  AAAI  2017
Goal:  Find  a  mapping  of  nodes  to  d-­‐dimensions  that  preserves   proximity  and  node  similarity Using  structure  +  attributes  (if  any)
Ahmed  et.  al  2017
A  (conditional)  attributed  walk  is  a  finite  sequence  of  adjacent   node  types  (words)  in  the  graph Ahmed  et.  al  2017
The  random  walk  traversed  link                            ,   Evaluating  next  step  at  node  V2
focus  vertex
Ahmed  et.  al  2017
G1 1 G2 3 2 G3 4 G4 5 6 G5 7 8 G6 9 G7 10 11 12 G9 15 G8 13 14 Network  Motifs:  Simple  Building  Blocks  of  Complex  Networks  – [Milo  et  al.  – Science  2002] The  Structure  and  Function  of  Complex  Networks  – [Newman  – Siam  Review  2003] Applied  to  food,  biologcal,  genetic,  neural,  web,  and  other  networks
§ Predict  which  pairs  of  nodes  are  likely  to  connect § Applications: social  network  analysis,  biological  networks,   terrorist  networks,  etc.
Deepwalk (DW)  – Perrozi et  al.  KDD  2014 node2vec    (N2V)  – Grover  et  al.  KDD  2016 LINE:  Tang  et  al.  – WWW  2015
1 2 4 8 12 16 0 2 4 6 8 10 12 14 16 Number of processing units Speedup socfb−MIT bio−dmela soc−gowalla tech−RL−caida web−wikipedia09 1 2 4 8 12 16 0 2 4 6 8 10 12 14 16 Number of processing units Speedup Strong  scaling  results Using  Intel  Xeon  E5-­‐2687W  server,  16  cores Motif  Counting
§ We  propose  a  generic  framework  for  learning  representation   in  large  attributed  graphs § Maps  nodes  based  on  Structural  similarity  +  proximity  +   attributes  (if  any) § Learns  universal  features  that  can  generalize  across   networks/graphs § Useful  for  inductive/transfer learning § Scalable  for  large  graphs
§ Generalizing  other  deep  graph  models § Theoretical  analysis   § Choice  of  mapping  functions § Impact  of  sampling  strategy   § Evaluation  on  other  ML  tasks
§ Efficient  estimation  of  word  representations  in  vector  space.  ICLR  2013  [Mikolov et.  al] § A  Framework  for  Generalizing  Graph-­‐based  Representation  Learning  Methods.  arXiv:1709.04596    2017  [Ahmed  et.  al] § Role  Discovery  in  Networks.  TKDE  2015  [Rossi  &  Ahmed] § A  Higher-­‐order  Latent  Space  Network  Model.  AAAI  2017  [Ahmed,  Rossi,  Willke,  Zhou] § node2vec:  Scalable  Feature  Learning  for  Networks.  KDD  2016  [Grover,  Leskovec] § DeepWalk:  online  learning  of  social  representations.  KDD  2014  [Perozzi,  Al-­‐Rafou,  Skiena] § Efficient  Graphlet Counting  for  Large  Networks.  ICDM  2015,  [Ahmed  et  al.] § Graphlet Decomposition:  Framework,  Algorithms,  and  Applications.  J.  Know.  &  Info.  2016  [Ahmed  et  al.] § Network  Motifs:  Simple  Building  Blocks  of  Complex  Networks.  Science  2002,  [Milo  et  al.] § Uncovering  Biological  Network  Function  via  Graphlet Degree  Signatures.  Cancer  Informatics  2008  [Milenković-­‐Pržulj] § Graph  Kernels.  JMLR  2010,  [Vishwanathan et  al.] § The  Structure   and  Function  of  Complex  Networks.  SIAM  Review  2003,  [Newman] § Biological  network  comparison  using  graphlet degree  distribution.  Bioinformatics  2007  [Pržulj] § Efficient  Graphlet Kernels  for  Large  Graph  Comparison.  AISTAT  2009  [Shervashidze et  al.] § Local  structure   in  social  networks.  Sociological  methodology  1976,  [Holland-­‐Leinhardt] § The  strength   of  weak  ties:  A  network  theory  revisited.  Sociological  theory 1983  [Granovetter]
Thank  you! Questions? nesreen.k.ahmed@intel.com http://nesreenahmed.com

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Representation Learning in Large Attributed Graphs

  • 1.
  • 2. -­‐ -­‐ -­‐ -­‐ -­‐ Social  network   Human  Disease  Network   [Barabasi 2007] Food  Web  [2007] Terrorist  Network [Krebs  2002]Internet  (AS)  [2005] Gene  Regulatory  Network   [Decourty 2008] Protein  Interactions   [breast  cancer] Political  blogs Power  grid
  • 3. input 0 … 1 … 0 … Feature   Engineering features 1 … 1 … 0 0 1 0 0 Learning   AlgorithmModel Prediction  Task Link  prediction Classification   Anomaly  detection
  • 4. input 0 … 1 … 0 … Feature   Engineering features 1 … 1 … 0 0 1 0 0 Learning   AlgorithmModel Prediction  Task Automatic   Feature  Learning Link  prediction Classification   Anomaly  detection
  • 5. § Goal:  Learn  representation  (features)  for  a  set  of  graph   elements  (nodes,  edges,  etc.) § Key  intuition:  Map  the  graph  elements  (e.g.,  nodes)  to  the   d-­‐dimension  space,  while  preserving  node  similarity § Use  the  features  for  any  downstream  prediction  task
  • 6. Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) DeepWalk Model Perrozi et  al.  KDD  2014
  • 7. Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) How  to  get  nearby  nodes? Perrozi et  al.  KDD  2014 Grover  et  al.  KDD  2016
  • 8. Recent  work:  Map  nodes  based  on  their  proximity  in  the   input  graph  – (nearby  nodes  are  close  together) § A  (conditional)  walk/path  is  a  finite  sequence  of  adjacent   vertices  in  the  graph How  to  get  nearby  nodes? Perrozi et  al.  KDD  2014 Grover  et  al.  KDD  2016
  • 9. V1 V3 V4 V2 V5 The  random  walk  traversed  link  V1  -­‐-­‐-­‐ V2 Evaluating  next  step  at  node  V2
  • 10. Mikolov et  al.  ICLR  2013 Perrozi et  al.  KDD  2014 focus  vertex
  • 11.
  • 12. § No  support  for  inductive/transfer  learning • features  are  learned  for  node  identities   • features  do  not  generalize  beyond  the  input  graph § Map  nodes  based  on  their  proximity  only § No  notion  of  attributes § No  notion  of  structural  similarity
  • 13. Communities:  cohesive  subsets  of  nodes Roles:  represent  structural  patterns -­‐ two  nodes  belong  to  the  same  role  if  they’ve  similar  structural  patterns Cj# Ci# Ck# Rossi  &  Ahmed  TKDE  2015 Ahmed  et  al.  AAAI  2017
  • 14. Goal:  Find  a  mapping  of  nodes  to  d-­‐dimensions  that  preserves   proximity  and  node  similarity Using  structure  +  attributes  (if  any)
  • 16. A  (conditional)  attributed  walk  is  a  finite  sequence  of  adjacent   node  types  (words)  in  the  graph Ahmed  et.  al  2017
  • 17. The  random  walk  traversed  link                            ,   Evaluating  next  step  at  node  V2
  • 20. G1 1 G2 3 2 G3 4 G4 5 6 G5 7 8 G6 9 G7 10 11 12 G9 15 G8 13 14 Network  Motifs:  Simple  Building  Blocks  of  Complex  Networks  – [Milo  et  al.  – Science  2002] The  Structure  and  Function  of  Complex  Networks  – [Newman  – Siam  Review  2003] Applied  to  food,  biologcal,  genetic,  neural,  web,  and  other  networks
  • 21. § Predict  which  pairs  of  nodes  are  likely  to  connect § Applications: social  network  analysis,  biological  networks,   terrorist  networks,  etc.
  • 22. Deepwalk (DW)  – Perrozi et  al.  KDD  2014 node2vec    (N2V)  – Grover  et  al.  KDD  2016 LINE:  Tang  et  al.  – WWW  2015
  • 23. 1 2 4 8 12 16 0 2 4 6 8 10 12 14 16 Number of processing units Speedup socfb−MIT bio−dmela soc−gowalla tech−RL−caida web−wikipedia09 1 2 4 8 12 16 0 2 4 6 8 10 12 14 16 Number of processing units Speedup Strong  scaling  results Using  Intel  Xeon  E5-­‐2687W  server,  16  cores Motif  Counting
  • 24. § We  propose  a  generic  framework  for  learning  representation   in  large  attributed  graphs § Maps  nodes  based  on  Structural  similarity  +  proximity  +   attributes  (if  any) § Learns  universal  features  that  can  generalize  across   networks/graphs § Useful  for  inductive/transfer learning § Scalable  for  large  graphs
  • 25. § Generalizing  other  deep  graph  models § Theoretical  analysis   § Choice  of  mapping  functions § Impact  of  sampling  strategy   § Evaluation  on  other  ML  tasks
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