Page 74 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks
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ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4




          Explainability of GNNs has been recently explored in two   In Section  5  we  describe  NetXplain,  the  proposed  explain‑
          main works. A  irst work emerging from the ML commu‑   ability solution.  Afterward,  Section 6 presents an evalu‑
          nity [10] analyzes a well‑known GNN model applied to a   ation of the accuracy and cost of NetXplain with respect
          chemistry  problem  to  quantify  the  impact  of  the  differ‑   to the state of the art. Finally, Section 7 presents a discus‑
          ent  input  elements  (atoms,  bonds)  on  the   inal  model pre‑   sion  on  possible  applications  of  the  proposed  explainabil‑
          dictions  (molecular  properties).  Likewise,  the  network‑   ity method, and Section 8 concludes the paper.
          ing  community  has  made  a   irst  attempt  to apply  a  simi‑
          lar  solution  to  several  network  optimization  use  cases   2.  BACKGROUND
          [3].  However,  both  solutions  are based  on  costly  iterative
          optimization  algorithms  that need to be executed for each
          sample  on  which  we  want  to  obtain  interpretations.   2.1  Graph neural networks
          Hence,  they  do  not  meet  the requirements  to  make  com‑
                                                               Graph neural networks are a novel neural network fam‑
          prehensive  analysis  over large  data  sets  and,  more  impor‑
                                                               ily  designed  to  operate  over  graph‑structured  data,  by
          tantly,  to  be  used  in  real‑time  applications.  To  address
                                                               capturing and modeling the inherent patterns in a graph.
          these limitations, this paper  proposes  NetXplain,  a  novel
                                                               This has resulted in an unprecedented predictive power
          real‑time  explainability  solution  for  GNNs.  NetXplain  in‑
                                                               in many applications where data is structured as graphs.
          troduces  a  novel  approach  where  we  use  a  GNN  that
                                                               Despite the several variants of GNNs introduced in recent
          learns,  from Tabula  Rasa,  how  to  interpret  the  outputs  of   years, in this paper we focus on Message‑Passing Neural
          another GNN  trained  for  a  speci ic  task  (e.g.,  routing  op‑
                                                               Networks  (MPNN)  [7],  as  they  represent  a  generic  GNN
          timization).  NetXplain  produces  human‑understandable
                                                               framework.
          interpretations of GNNs comparable to those of state‑of‑
                                                               MPNN operates over a graph G, in which nodes    ∈    are
          the‑art  solutions  [10,  3].  However,  it  makes  this  at  a much
                                                               characterized  with  some  initial  features     .   First,  the
                                                                                                       
          more  limited  cost.  In  our  evaluation,  we  apply NetXplain   0
                                                               hidden‑state ℎ of each  node     ∈     are initialized us‑
                                                                              
          to  RouteNet  [12],  a  GNN  model  that  predicts the  per‑path
                                                               ing their input node features    .   Once each element    of
                                                                                            
          delay  given  a  network  snapshot  as  input (i.e., topology +                 0
                                                               the graph has its hidden‑state ℎ initialized, they proceed
                                                                                            
          routing + traf ic matrix). To this end, we  irst  train  NetX‑
                                                               to the message‑passing phase, which shall be repeated a
          plain  on  a  data  set  with  samples produced by Metis [3].
                                                               given number of times    . Fig. 1 illustrates how the mes‑
          This training is done over a data set of limited size –5 to
                                                               sage passing phase works. In each iteration    of the algo‑
          10% of the original data set used in  RouteNet  [12].  Then,
                                                               rithm, every node    receives a message from each of its
          we  validate  the  generalization power  of  our  GNN‑based   neighbors    ∈     (  ).   In MPNN, messages are generated
          method,  by  applying  it  to network  scenarios  fundamen‑
                                                               using a function   (·) computed with the hidden state of
          tally  different  from  those seen during training. The evalu‑
                                                               the neighbor node. Then, once every node    has received
          ation results reveal the feasibility  to  train  NetXplain  over
                                                               the  messages  from  its  immediate  neighbors,  these  mes‑
          a  small  dataset produced by costly explainability solutions
                                                               sages  are  combined  with  an  aggregation  function    (·)  pro‑
          (e.g., [10, 3]), and  be  able  to  apply  it  over  a  wide  variety
                                                               ducing a  ixed‑size output (e.g., an element‑wise summa‑
          of  network scenarios. This eventually enables us to meet
                                                               tion).
          the needed requirements to make a comprehensive anal‑
                                                               Finally, the algorithm reaches the update phase, in which
          ysis of the safe operational range of GNN solutions at a
                                                               nodes use the aggregated information received from their
          limited cost. In this context, we show that NetXplain far
                                                               neighbors to update their own hidden states via the up‑
          outperforms state‑of‑the‑art  algorithms  in  terms  of  com‑
                                                               date function   (·).
          putational cost, running more than 3 orders of magnitude
                                                               Formally, the message passing at a given iteration    is de‑
          faster on average than Metis [3] when applied to samples
                                                                ined as:
          of three real‑world  network  topologies  up  to  24  nodes.                                       (1)
                                                                                    ,    =   (ℎ , ℎ ,    )
                                                                                          
                                                                                               ,  
                                                                                             
          As  an  example,  this  explainability  solution  can  be  used
                                                                                 +1  =   ({    |    ∈   (  )})  (2)
          as  follows:  given  a  GNN‑based  solution  and  a  network                   ,  
                                                                                          
          scenario, NetXplain points to the particular network ele‑             ℎ   +1  =   (ℎ ,         +1 )  (3)
                                                                                   
                                                                                          
          ments (e.g., devices, links, paths) that mostly affected the
                                                               In these equations,  functions   (·) and   (·) can be com‑
          output  decisions  of  the  GNN model. This can be help‑
                                                               puted through a universal function approximator, such as
          ful  for  many  different  use  cases,  including:  (  )  test  &
                                                               neural networks (e.g., feed‑forward NN or recurrent NN).
          troubleshooting of GNN‑based solutions, (    ) reverse en‑
                                                               After     message passings, the hidden states of nodes typ‑
          gineering, or (      ) improving network optimization solu‑
                                                               ically converge to some  ixed values [6].  Thus,  these  i‑
          tions.
                                                               nal hidden states pass through a readout function   (·) that
          The remainder of this paper is structured as follows. First,
                                                               computes the output of the GNN model.   (·) automatically
          Section 2 introduces the fundamental principles of GNNs
                                                               learns the mapping from hidden‑state representations to
          and  their  application  to  networking.   Then,  Section  3
                                                               the output labels of the model   :
          presents the related work on explainability for GNNs.
                                                                                         
                                                                                  ̂    =   (ℎ |    ∈    )    (4)
                                                                                         
          58                                 © International Telecommunication Union, 2021
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