Pith. sign in

Paper Citation Record · LEDGER

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2412.18720.

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

pith.paper-citation-record.v1
2412.18720 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:36:34.692065Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation ddcc8fd1-b6e3-4166-8a08-7a59b305eca0 · outbound

This paper cites Accessed: September 20, 2024.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Accessed: September 20, 2024

Reference 1

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Observation 4f42c6ee-0f16-4021-8d4b-c93e1374a7c2 · outbound

This paper cites Lightgcl: Simple yet effective graph contrastive learning for recommendation.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Lightgcl: Simple yet effective graph contrastive learning for recommendation

Reference 2

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This paper cites Structural balance: a generalization of heider’s theory.Psychological review, 63(5):277, 1956.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Structural balance: a generalization of heider’s theory.Psychological review, 63(5):277, 1956

Reference 3

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Observation 396ec514-2c56-47ed-917b-4e304ee1d160 · outbound

This paper cites Random walk with restart on hypergraphs: fast computation and an application to anomaly detection.Data Min.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Random walk with restart on hypergraphs: fast computation and an application to anomaly detection.Data Min

Reference 4

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This paper cites Balance in signed bipartite networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Balance in signed bipartite networks

Reference 5

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This paper cites Signed graph convolutional networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Signed graph convolutional networks

Reference 6

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This paper cites node2vec: Scalable feature learning for networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs node2vec: Scalable feature learning for networks

Reference 7

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Unresolved cited work

Reference 8

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Observation 876f41e1-9f49-46c8-8b48-25b30637af1c · outbound

This paper cites Maxwell Harper and Joseph A.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Maxwell Harper and Joseph A

Reference 9

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Unresolved cited work

Reference 10

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This paper cites Lightgcn: Simplifying and powering graph convolution network for recommendation.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 11

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Observation 358dbc9b-7594-4759-bcc8-764fcdef624c · outbound

This paper cites Signed bipartite graph neural networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Signed bipartite graph neural networks

Reference 12

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This paper cites Signed graph attention networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Signed graph attention networks

Reference 13

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This paper cites SDGNN: learning node representation for signed directed networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs SDGNN: learning node representation for signed directed networks

Reference 15

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This paper cites Zoom-svd: Fast and memory efficient method for extracting key patterns in an arbitrary time range.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Zoom-svd: Fast and memory efficient method for extracting key patterns in an arbitrary time range

Reference 16

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This paper cites Random walk-based ranking in signed social networks: model and algorithms.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Random walk-based ranking in signed social networks: model and algorithms

Reference 17

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This paper cites Personalized ranking in signed networks using signed random walk with restart.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Personalized ranking in signed networks using signed random walk with restart

Reference 18

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Source-reported events for the cited work

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This paper cites Bepi: Fast and memory-efficient method for billion-scale random walk with restart.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Bepi: Fast and memory-efficient method for billion-scale random walk with restart

Reference 19

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Source-reported events for the cited work

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach

Reference 20

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Signed Graph Diffusion Network

Reference 21

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs SIDE: representation learning in signed directed networks

Reference 22

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This paper cites Learning disentangled representations in signed directed graphs without social assumptions.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Learning disentangled representations in signed directed graphs without social assumptions

Reference 23

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Universal graph contrastive learning with a novel laplacian perturbation

Reference 24

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Observation 4065cd8b-cbf8-4fde-9a50-4f14afdc3c16 · outbound

This paper cites Time-aware random walk diffusion to improve dynamic graph learning.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Time-aware random walk diffusion to improve dynamic graph learning

Reference 25

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This paper cites Mule: Multi-grained graph learning for multi- behavior recommendation.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Mule: Multi-grained graph learning for multi- behavior recommendation

Reference 26

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This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Deeper insights into graph convolutional networks for semi-supervised learning

Reference 27

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Learning signed network embedding via graph attention

Reference 28

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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This paper cites McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel

Reference 29

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Source-reported events for the cited work

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This paper cites A noise-filtering method for link prediction in complex networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs A noise-filtering method for link prediction in complex networks

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs The pagerank citation ranking: Bringing order to the web

Reference 31

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Source-reported events for the cited work

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This paper cites A comparative study of matrix factorization and random walk with restart in recommender systems.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs A comparative study of matrix factorization and random walk with restart in recommender systems

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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This paper cites Clustered low rank approximation of graphs in information science applications.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Clustered low rank approximation of graphs in information science applications

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3207f16e-43c1-4694-bd6a-f987c73dd1cc · outbound

This paper cites The graph neural network model.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs The graph neural network model

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0f7304a6-26b1-42c8-be4c-296ea046b124 · outbound

This paper cites BEAR: block elimination approach for random walk with restart on large graphs.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs BEAR: block elimination approach for random walk with restart on large graphs

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.839483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.671043Z digest=sha256:8babd24b87e51939ec3a8a71d63ea8347d181c7fd7d61c75aefad1bd0fd05e0e

Observation 2a48ce4e-3fd8-45c2-b255-91aba069988d · outbound

This paper cites SGCL: contrastive representation learning for signed graphs.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs SGCL: contrastive representation learning for signed graphs

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.823448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.675184Z digest=sha256:abb55a65a765817233872f084488b09733740de720bde7e9e9429de86fe9381b

Observation ddb835e7-c68d-40a9-9572-12517f6a7839 · outbound

This paper cites Link prediction with signed latent factors in signed social networks.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Link prediction with signed latent factors in signed social networks

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.800303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.679795Z digest=sha256:9cb46a02b40693f685e87f6ba47b70a17afd15581c1cb212bc05de452e652bba

Observation e6062cd1-af49-40b4-8310-d757c9ec3766 · outbound

This paper cites Dual-branch density ratio estimation for signed network embedding.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Dual-branch density ratio estimation for signed network embedding

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.784983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.683906Z digest=sha256:de2b4e94e442af34ef00889714ff1f98f7acdf96511fe3c0d29dd1abd8bc42f5

Observation f65b5480-24dd-4d04-a6c8-1f9aebf89200 · outbound

This paper cites Graph contrastive learning with augmentations.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Graph contrastive learning with augmentations

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.769633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.687744Z digest=sha256:1e1f43d68d55a294a8988c0713bf8ebe72a81a7a6362da09ff6b8dd6642aa211

Observation 2a4279cb-e139-46f0-833b-d90bcc31ba93 · outbound

This paper cites Contrastive learning for signed bipartite graphs.

Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs Contrastive learning for signed bipartite graphs

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T04:36:34.756429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T04:36:34.692065Z digest=sha256:71a5b97a591f962d245ae012c58a4b2dcfb84fceb045dd82b5bccfd68d9cbae0

Pith citing papers

No inbound Pith citation observations are available.