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fixed wording error
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alto
alto
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Oversized comment warning

You could phrase your problems in terms of link prediction. There is a lot of literature on this for things like social networks and whatnot. The problem that is going to make all (almost all) of of that standard techniques I'm aware of inapplicable is that you've assumed you don't know any of the links for the node you're trying to infer the links for. Typically in link prediction, you assume you know some of the links, and want to infer others.

As you've framed the problem, the graph structure is actually irrelevant. You're simply trying to predict, for a given medicine, which other medicines can be combined with it to make it more effective. This is just multi-label classification. In other words if $M$ is your set of medicines, and for medicine $m \in M$ you have features $\phi(m)$, you want to learn a function $$ h \colon \phi(M) \to 2^M, $$ where $\phi(M) = \{\phi(m) \colon m \in M\}$. I somewhat doubt if that would be very useful or effective in your case given that you only have a single feature for each featuremedicine. It might be worth a try.

Oversized comment warning

You could phrase your problems in terms of link prediction. There is a lot of literature on this for things like social networks and whatnot. The problem that is going to make all (almost all) of of that standard techniques I'm aware of inapplicable is that you've assumed you don't know any of the links for the node you're trying to infer the links for. Typically in link prediction, you assume you know some of the links, and want to infer others.

As you've framed the problem, the graph structure is actually irrelevant. You're simply trying to predict, for a given medicine, which other medicines can be combined with it to make it more effective. This is just multi-label classification. In other words if $M$ is your set of medicines, and for medicine $m \in M$ you have features $\phi(m)$, you want to learn a function $$ h \colon \phi(M) \to 2^M, $$ where $\phi(M) = \{\phi(m) \colon m \in M\}$. I somewhat doubt if that would be very useful or effective in your case given that you only have a single feature for each feature. It might be worth a try.

Oversized comment warning

You could phrase your problems in terms of link prediction. There is a lot of literature on this for things like social networks and whatnot. The problem that is going to make all (almost all) of of that standard techniques I'm aware of inapplicable is that you've assumed you don't know any of the links for the node you're trying to infer the links for. Typically in link prediction, you assume you know some of the links, and want to infer others.

As you've framed the problem, the graph structure is actually irrelevant. You're simply trying to predict, for a given medicine, which other medicines can be combined with it to make it more effective. This is just multi-label classification. In other words if $M$ is your set of medicines, and for medicine $m \in M$ you have features $\phi(m)$, you want to learn a function $$ h \colon \phi(M) \to 2^M, $$ where $\phi(M) = \{\phi(m) \colon m \in M\}$. I somewhat doubt if that would be very useful or effective in your case given that you only have a single feature for each medicine. It might be worth a try.

Source Link
alto
alto
  • 3.6k
  • 21
  • 21

Oversized comment warning

You could phrase your problems in terms of link prediction. There is a lot of literature on this for things like social networks and whatnot. The problem that is going to make all (almost all) of of that standard techniques I'm aware of inapplicable is that you've assumed you don't know any of the links for the node you're trying to infer the links for. Typically in link prediction, you assume you know some of the links, and want to infer others.

As you've framed the problem, the graph structure is actually irrelevant. You're simply trying to predict, for a given medicine, which other medicines can be combined with it to make it more effective. This is just multi-label classification. In other words if $M$ is your set of medicines, and for medicine $m \in M$ you have features $\phi(m)$, you want to learn a function $$ h \colon \phi(M) \to 2^M, $$ where $\phi(M) = \{\phi(m) \colon m \in M\}$. I somewhat doubt if that would be very useful or effective in your case given that you only have a single feature for each feature. It might be worth a try.