Paper Citation Record · LEDGER

Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.03464.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.03464 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:52:21.348156Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T22:16:16.203843Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e7ffd46-b6cc-444e-b581-20f2d5ee5a1b · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Reference 140

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:33:39.627070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c2196801-05f2-4025-b613-f6d8195c2069 · inbound

Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification cites this paper.

Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:26:01.904450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6428de6e-a1dc-4815-86bb-8a2288c084f9 · inbound

Closed-Form Node Classification with Exact Graph Unlearning cites this paper.

Closed-Form Node Classification with Exact Graph Unlearning Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:54:00.810597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b5c6e28e-476c-47c9-8057-cbf22d7eb4d6 · inbound

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs cites this paper.

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:16:16.205498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.