Row 5722

Row ID: 5722 | Dataset Entry | Axioma AXP Content Repository

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Hey buddies,

I want to implement a TF-GNN where both inputs and outputs are graphs, i.e., I give the model a three-node graph with some attributes at nodes/edges and get as output the same 3-node graph with a single attribute per node. For instance, the three input nodes are three cities (attributes like population, boolean for is holiday, etc ) with their connecting roads as edges (attributes like trains scheduled for that day, etc.) and I get as output a "congestion" metric for each city.

Does anyone know about papers/tutorials with such implementation? Not sure if it's something available. So far I've only found graph classification or single-attribute regression.

Thanks!

FieldValue
text Hey buddies, I want to implement a TF-GNN where both inputs and outputs are graphs, i.e., I give the model a three-node graph with some attributes at nodes/edges and get as output the same 3-node graph with a single attribute per node. For instance, the three input nodes are three cities (attributes like population, boolean for is holiday, etc ) with their connecting roads as edges (attributes like trains scheduled for that day, etc.) and I get as output a "congestion" metric for each city. Do…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2024-05-03
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{
  "text": "Hey buddies,\n\nI want to implement a TF-GNN where both inputs and outputs are graphs, i.e., I give the model a three-node graph with some attributes at nodes/edges and get as output the same 3-node graph with a single attribute per node. For instance, the three input nodes are three cities (attributes like population, boolean for is holiday, etc ) with their connecting roads as edges (attributes like trains scheduled for that day, etc.) and I get as output a \"congestion\" metric for each city.\n\nDoes anyone know about papers/tutorials with such implementation? Not sure if it's something available. So far I've only found graph classification or single-attribute regression.\n\nThanks!",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2024-05-03",
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}
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