We lifted 14 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| kage08/graph_sample_rl | canonical | 11 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| alias_sample | Ran | kage08/graph_sample_rl/ge/alias.py code served (permissive licence) · get_code("5a75781caceb9236") |
| create_alias_table | Ran | kage08/graph_sample_rl/ge/alias.py code served (permissive licence) · get_code("8985b3c4085e0103") |
| f_all_influmax_multlinear | Ran | kage08/graph_sample_rl/icm.py code served (permissive licence) · get_code("08e4f8e636494055") |
| fanin_init | Ran | kage08/graph_sample_rl/diffpool/utils.py code served (permissive licence) · get_code("02725759d7e0f793") |
| greedy | Ran | kage08/graph_sample_rl/utils.py code served (permissive licence) · get_code("249aca583ea3bfdd") |
| greedy_icm | Ran | kage08/graph_sample_rl/utils.py code served (permissive licence) · get_code("3499b2e151784dcd") |
| indicator | Ran | kage08/graph_sample_rl/icm.py code served (permissive licence) · get_code("8ef536d5c256abeb") |
| nmf_embed1 | Ran | kage08/graph_sample_rl/expts/graphembed.py code served (permissive licence) · get_code("e8fd33e59b22c589") |
| projection_simplex_sort | Ran | kage08/graph_sample_rl/utils.py code served (permissive licence) · get_code("56d4fa85a26c8c89") |
| random_sbm | Ran | kage08/graph_sample_rl/expts/gengraph.py code served (permissive licence) · get_code("86dd7336e2145c1a") |
| sample_live_icm | Ran | kage08/graph_sample_rl/icm.py code served (permissive licence) · get_code("22daae4291d1af18") |
| csv_to_graph | Not yet run | kage08/graph_sample_rl/expts/gendata.py code served (permissive licence) · get_code("3851cddf27b1234f") |
| nmf_embed | Not yet run | kage08/graph_sample_rl/expts/graphembed.py code served (permissive licence) · get_code("0e46fd92680f3062") |
| node_embed | Not yet run | kage08/graph_sample_rl/expts/graphembed.py code served (permissive licence) · get_code("6727fbd13179d326") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1907.11625")
get_code_for_paper("1907.11625")
have("1907.11625")
Connect an agent — have() is free.