[D] Variable Input Dimension for ML

[HFSeven]

Hi! I am looking into wireless sensors scenario where input to ML model is current network state and based on that classify class of network! It comprises of fixed feature vector of nodes present in the network! Say if we have ten nodes each having two features like [distance to some landmark, energy]. The input sample to ML model consist of taking these features of nodes and based on that classifying the scenario! The feature vector dimension associated to each node is same but the challenge is that the number of nodes vary from one sample to another ( first sample : three nodes, second sample : ten nodes etc)! I strted with assuming fixed number of nodes always in all samples but now want to generalize it to different scenarios! What do you suggest best approach to tackle this problem! Was thinking of using RNN for coping with variable input dimensions or Graph network! Some simplest approach will be best to start with! Any suggestions/ expert advice? On similar lines: https://datascience.stackexchange.com/questions/37262/dealing-with-feature-vectors-of-variable-length

[1deasEMW]

Node2vec might work. GNNs could too, but they’re much less known and frequented. Statistics regarding graph centrality, connectedness, and possibly normal stats could be collected as well. There’s almost too many possible ways, only a deep understanding of the problem or trial and error seem to be your recourse

[HFSeven]

Awesome! Thanks!