Body: <p>I already did graph-level classification using heterogeneous hypergraph learning in an ICDM paper last year. However, I now want to extend it for dynamic graphs, i.e. the task is dynamic graph-level classification. I used MUTAG, ENZYMES, PROTEINS, COLLAB, IMDB-BINARY datasets that have different number of labelled graphs and then we used accuracy for evaluation. However, the dynamic graphs datasets like Facebook, Enron, Slashot, Formula 1 (Reddit), though, have different timestamped graphs but no labels.</p>
  <p>I am just curious to know if this is a feasible problem to solve, i.e. dynamic graph-level classification without labels?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-02T14:52:52.213Z
Comments.Text: Can you explain what a "dynamic graph" is? What exactly is dynamic? Do you mean that the vertices and connections change over time? Also, what do you mean by "without labels"? What kind of labels do you have? Are you trying to classify the vertices or edges and you mean that you don't have some labels for the vertices or edges? I've also changed your title to be what I think is your question. Make sure that's the case.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-02T15:55:51.033Z
Comments.Text: @nbro yes, correct, a dynamic graph has nodes/edges that may change over time e.g., for a graph `G_1`, some nodes and edges can be added or deleted at `t_2` as compared to `t_1`.  "Without labels" means there exist no labels for each timestamp of the graph. However, in the static graph-level datasets like MUTAG, for each graph in this dataset, we have a label as "mutagenic", or "non-mutagenic". Therefore, we can use accuracy or F1-score as an evaluation of our classification algorithm. I want to label/classify the whole graphs (dynamic) not individual nodes or edges.
Comments.UserDisplayName: maliks
Tags: <classification><graph-neural-networks><graphs><representation-learning>
Title: Is it feasible to solve dynamic graph-level classification without labels?
UserDisplayName: maliks
ThreadText: # Is it feasible to solve dynamic graph-level classification without labels?
  
  (Asked by: username_0 on 2024-04-02)
  
  I already did graph-level classification using heterogeneous hypergraph learning in an ICDM paper last year. However, I now want to extend it for dynamic graphs, i.e. the task is dynamic graph-level classification. I used MUTAG, ENZYMES, PROTEINS, COLLAB, IMDB-BINARY datasets that have different number of labelled graphs and then we used accuracy for evaluation. However, the dynamic graphs datasets like Facebook, Enron, Slashot, Formula 1 (Reddit), though, have different timestamped graphs but no labels.
  
  I am just curious to know if this is a feasible problem to solve, i.e. dynamic graph-level classification without labels?
  
  **Tags:** classification, graph-neural-networks, graphs, representation-learning
  
  > **Comments:**
  > **username_1**: Can you explain what a "dynamic graph" is? What exactly is dynamic? Do you mean that the vertices and connections change over time? Also, what do you mean by "without labels"? What kind of labels do you have? Are you trying to classify the vertices or edges and you mean that you don't have some labels for the vertices or edges? I've also changed your title to be what I think is your question. Make sure that's the case.  
  > **username_0**: @username_1 yes, correct, a dynamic graph has nodes/edges that may change over time e.g., for a graph `G_1`, some nodes and edges can be added or deleted at `t_2` as compared to `t_1`.  "Without labels" means there exist no labels for each timestamp of the graph. However, in the static graph-level datasets like MUTAG, for each graph in this dataset, we have a label as "mutagenic", or "non-mutagenic". Therefore, we can use accuracy or F1-score as an evaluation of our classification algorithm. I want to label/classify the whole graphs (dynamic) not individual nodes or edges.  
  
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  ## 0 answers
  
  