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DaltonSC2 t1_iusyvz6 wrote

Reply to comment by miguelventura in [D] Graph neural networks by No_Captain_856

>though connections may carry data themselves, in which case an adjacency matrix isn't enough

does using pointwise correlation as the adj matrix not give enough information to learn the different categories?

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master3243 t1_iutotkj wrote

It's not just about learning different categories.

Imagine you're trying to study a social network of people, take twitter users for example, the individual nodes will probably be the users and the data associated with them (past tweets, bio, etc) while the edges would be the connection between users that you care about (e.g. A follows B, or A tweeted at B, or A retweeted post by B, etc.) and you can see how each of those connections carries information other than just a binary yes or no (e.g. When did A follow B? How many previous tweets did A see of B? How many followers did B have at the time? How many tweets did B have at that time?)

You can see how an individual edge can carry an extremely rich feature vector between nodes A and B where those features are separate from the features belonging to either node A and B themselves. Thus, it's possible that a binary adjacency matrix would not be enough to capture the intrinsic properties of that system.

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After-Advertising-61 t1_iuu5h9b wrote

Have you ever encountered data or a scenario where the underlying process might be well represented by two types of edges? e.g. "Twitter replies to agree/support" vs "Twitter replies to disagree/condemn." Two phase networks have like this are great for representing all kinds of resonance modes (like plasmon), but I haven't quite found a good data science or applied stat application. My intuition is that two party political discourse might be well represented in this style.

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telimektar t1_iuv5hi9 wrote

What you are describing is often referred to as heterogeneous graphs or knowledge graph, plenty of examples out there. Model wise there is a full literature dedicated to the topic.

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