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pith:LOO7PW5M

pith:2026:LOO7PW5MFNSJU4DQFHXJAL4C3K

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SP-GCRL: Influence Maximization on Incomplete Social Graphs

Haohua Niu, Hao Li, Jiao Liang, Lingfeng Zhang, Luca Rossi, Yuxuan Yang, Zongfu Luo

SP-GCRL learns end-to-end seed selection policies for influence maximization on incomplete social graphs using contrastive representations and a nonlinear diffusion model.

arxiv:2605.12513 v1 · 2026-03-31 · cs.SI · cs.AI

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Experiments on multiple real-world networks show that SP-GCRL achieves significant gains over heuristic and learning-based baselines across budgets and topologies, while maintaining strong large-scale scalability.

C2weakest assumption

The proposed social-propagation-aware nonlinear diffusion function correctly models reinforcement, diminishing returns, and probability drift under repeated exposure in real incomplete graphs.

C3one line summary

SP-GCRL combines a nonlinear social diffusion model, dual-view contrastive learning for robust node embeddings, a GAT surrogate, and DDQN to learn end-to-end seed selection policies for influence maximization under partial graph observability.

References

36 extracted · 36 resolved · 0 Pith anchors

[1] Science advances10(15), eadh4439 (2024) 2024

[2] In: 2018 IEEE/WIC/ACM Interna- tional Conference on Web Intelligence (WI) 2018

[3] Proceedings of the National Academy of Sciences115(37), 9216–9221 (2018) 2018

[4] In: Uncertainty in Artificial In- telligence 2021

[5] IEEE Transactions on Compu- tational Social Systems11(2), 2210–2221 (2023) 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.956899Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5b9df7dbac2b649a707029ee902f82da9f94f5b7a70ab83d5323c8e5363bccd9

Aliases

arxiv: 2605.12513 · arxiv_version: 2605.12513v1 · doi: 10.48550/arxiv.2605.12513 · pith_short_12: LOO7PW5MFNSJ · pith_short_16: LOO7PW5MFNSJU4DQ · pith_short_8: LOO7PW5M

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LOO7PW5MFNSJU4DQFHXJAL4C3K \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5b9df7dbac2b649a707029ee902f82da9f94f5b7a70ab83d5323c8e5363bccd9

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.SI",
    "submitted_at": "2026-03-31T09:44:20Z",
    "title_canon_sha256": "f7f7ece560f48ffc336b209f7d877c8346cbf7d49e8e70bbee90620e3b2a4188"
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