We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| petergriffinjin/heterformer | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| acc | Ran | petergriffinjin/heterformer/downstream/classification.py code served (permissive licence) · get_code("5f6cca96082d0328") |
| dcg_score | Ran | petergriffinjin/heterformer/downstream/classification.py code served (permissive licence) · get_code("43e79e8e0904d5d8") |
| ndcg_score | Ran | petergriffinjin/heterformer/downstream/classification.py code served (permissive licence) · get_code("342ff9cc266246eb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure. As pretrained language models (PLMs) have demonstrated their effectiveness in obtaining widely generalizable text representations, a substantial amount of effort has been made to incorporate PLMs into representation learning on text-rich networks. However, few of them can jointly consider heterogeneous structure (network) information as well as rich textual semantic information of each node effectively. In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. Specifically, we inject heterogeneous structure information into each Transformer layer when encoding node texts. Meanwhile, Heterformer is capable of characterizing node/edge type heterogeneity and encoding nodes with or without texts. We conduct comprehensive experiments on three tasks (i.e., link prediction, node classification, and node clustering) on three large-scale datasets from different domains, where Heterformer outperforms competitive baselines significantly and consistently.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.10282")
get_code_for_paper("2205.10282")
have("2205.10282")
Connect an agent — have() is free.