Paper Citation Record · LEDGER

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach

As of 22 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.01949.

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

pith.paper-citation-record.v1
2412.01949 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:06:45.085179Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

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

43 of 43 outbound references displayed

verified exact1
verified fuzzy39
unresolved3
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b24481c5-faf9-427c-936c-4d083a973fd8 · outbound

This paper cites A social force evacuation model with the leadership effect.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A social force evacuation model with the leadership effect

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.440328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4e8dbb47-e234-4667-862f-c59626278951 · outbound

This paper cites Seeds selection for spreading in a weighted cascade model.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Seeds selection for spreading in a weighted cascade model

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.433965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 305a4ea7-0cca-4cdc-8f9f-7786a3c259f3 · outbound

This paper cites A mathematical theory of communication.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A mathematical theory of communication

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.427456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b37262c-bcf7-428d-94d5-ee19e7e3d814 · outbound

This paper cites Key node ranking in complex networks: A novel entropy and mutual information-based approach.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Key node ranking in complex networks: A novel entropy and mutual information-based approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.420905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b9e995e-96b4-4100-9173-979acf50f6f4 · outbound

This paper cites Maximizing the spread of influence through a social network.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Maximizing the spread of influence through a social network

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.413684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c24219a1-96af-40f3-b067-500ddfa7eaad · outbound

This paper cites Identifying spreading influence nodes for social networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Identifying spreading influence nodes for social networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.407562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15654c4f-8476-474e-950e-47670d96c92e · outbound

This paper cites Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Influence maximization frameworks, performance, challenges and directions on social network: A theoretical study

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.400072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 156d0148-d4ac-4d29-99ea-6793945f5487 · outbound

This paper cites Top influencers can be identified universally by combining classical centralities.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Top influencers can be identified universally by combining classical centralities

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.394256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 286273c3-a2a6-48aa-a4b7-46b727082941 · outbound

This paper cites A machine learning-based approach for vital node identification in complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A machine learning-based approach for vital node identification in complex networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.388948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41ef5264-7a1d-464c-9ad6-d692f6d5ba87 · outbound

This paper cites A machine learning based framework for identifying influential nodes in complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A machine learning based framework for identifying influential nodes in complex networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.383495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5fe8331b-d298-4f31-870e-1dd26e101e0b · outbound

This paper cites Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.373045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8329dd3f-a476-4a6f-8787-8d0f3efcfb6e · outbound

This paper cites Influence maximization in complex networks through supervised machine learning.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Influence maximization in complex networks through supervised machine learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.366714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e437cb94-2347-465d-85ee-3598dc5be218 · outbound

This paper cites Finding influencers in complex networks: An effective deep reinforcement learning approach.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Finding influencers in complex networks: An effective deep reinforcement learning approach

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.359893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 66ff0e0f-b0a4-44f4-ba9c-37366fdad29b · outbound

This paper cites Topological to deep learning era for identifying influencers in online social networks: A systematic review.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Topological to deep learning era for identifying influencers in online social networks: A systematic review

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.353715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34e98df1-bae9-47b7-afc3-9d5e6a11b6dd · outbound

This paper cites Centrality in social networks: Conceptual clarification.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Centrality in social networks: Conceptual clarification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.346721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be2b9971-e386-48c3-bc8f-390d0153b04d · outbound

This paper cites Identifying influential nodes in complex networks based on multiple local attributes and information entropy.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Identifying influential nodes in complex networks based on multiple local attributes and information entropy

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.340061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 04e681a0-15e2-45f0-99e1-4b611e10af12 · outbound

This paper cites How to identify the most powerful node in complex networks? A novel entropy centrality approach.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach How to identify the most powerful node in complex networks? A novel entropy centrality approach

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.332717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd7b2e6d-9539-4731-9d49-ec071efa0b10 · outbound

This paper cites Weighted kshell degree neighborhood method: An approach independent of completeness of global network structure for identifying the influential spreaders.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Weighted kshell degree neighborhood method: An approach independent of completeness of global network structure for identifying the influential spreaders

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.323588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a28d8475-72db-4575-a5b6-b011956da2ef · outbound

This paper cites Network-based high level data classification.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Network-based high level data classification

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.315047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd8f0bba-9179-41aa-bf3b-d3af99050973 · outbound

This paper cites Deep-learning-based identification of influential spreaders in online social networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Deep-learning-based identification of influential spreaders in online social networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.305078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64ba3281-96a7-4d84-a742-6db00401353a · outbound

This paper cites Influencer Detection with Dynamic Graph Neural Networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Influencer Detection with Dynamic Graph Neural Networks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:06:45.131786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a234bd1a-6f35-4658-aa90-8b0f7382bb60 · outbound

This paper cites Perturb and combine to identify influential spreaders in real-world networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Perturb and combine to identify influential spreaders in real-world networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.296992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 342aff2a-2aa9-4ff0-a2a1-9eae1f572e97 · outbound

This paper cites Leveraging neighborhood and path information for influential spreaders recognition in complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Leveraging neighborhood and path information for influential spreaders recognition in complex networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.288946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a96b4c8b-29a3-430c-a8a7-e6baf7dfaa37 · outbound

This paper cites LSS: A locality-based structure system to evaluate the spreader’s importance in social complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach LSS: A locality-based structure system to evaluate the spreader’s importance in social complex networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.281613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aae2880e-f476-4127-a819-27af63a52c12 · outbound

This paper cites Least squares quantization in PCM.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Least squares quantization in PCM

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.274033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae8d8627-e986-4d8a-8581-59e6505f097f · outbound

This paper cites A Tutorial on Spectral Clustering.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A Tutorial on Spectral Clustering

Reference 26

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Source-reported events for the cited work

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Observation 4504991f-f068-44f1-b062-2942c930fca3 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.266090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41e9907d-147c-406f-97b1-58d167299372 · outbound

This paper cites Centrality in networks: I.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Centrality in networks: I

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.256053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 81bcd341-6215-4ca4-b1f9-0654260e1e64 · outbound

This paper cites On variants of shortest-path betweenness centrality and their generic computation.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach On variants of shortest-path betweenness centrality and their generic computation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.247569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9880ce2d-322d-41b0-b899-bd58028c470f · outbound

This paper cites Hierarchy measure for complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Hierarchy measure for complex networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.238946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8b96341-3595-48fc-ab99-d0581246222e · outbound

This paper cites Identifying a set of influential spreaders in complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Identifying a set of influential spreaders in complex networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.230107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f44812f2-3c55-48e7-88f6-1c3c7be0bdc3 · outbound

This paper cites Scientific collaboration networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Scientific collaboration networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.221996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b084874-580f-42d2-8891-bb55b64ffae1 · outbound

This paper cites Generalizations of the clustering coefficient to weighted complex networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Generalizations of the clustering coefficient to weighted complex networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.214060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 240e7997-96fb-47e3-8dd8-a695c7a1cc2d · outbound

This paper cites An O(m) Algorithm for Cores Decomposition of Networks.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach An O(m) Algorithm for Cores Decomposition of Networks

Reference 34

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Source-reported events for the cited work

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Observation 81362e32-16a5-4765-82ce-667c9fc5a5fd · outbound

This paper cites Power and centrality: A family of measures.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Power and centrality: A family of measures

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.205141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cbc019d4-dc5e-40af-b6dc-7dff82d2dcee · outbound

This paper cites The PageRank Citation Ranking: Bringing Order to the Web; Technical Report; Stanford InfoLab: Stanford, USA 1999.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach The PageRank Citation Ranking: Bringing Order to the Web; Technical Report; Stanford InfoLab: Stanford, USA 1999

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.196338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b83f6ff7-01ec-4eb9-bd54-6ec504787d41 · outbound

This paper cites Axioms for centrality.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Axioms for centrality

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.187244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 226d6c03-f5b3-436a-888a-b71127122ee2 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Revisiting semi-supervised learning with graph embeddings

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.177496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a30b3da0-8375-43c1-b586-05779f8c80db · outbound

This paper cites Multi-scale attributed node embedding.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Multi-scale attributed node embedding

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.168607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9a16f44-5cf5-458b-953d-acf907108185 · outbound

This paper cites Machine Learning: Algorithms, Real-World Applications and Research Directions.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Machine Learning: Algorithms, Real-World Applications and Research Directions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T00:06:45.077209Z

Source-reported events for the cited work

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Observation dec41bc7-b6fd-4e0d-a2a4-d6422c125e0b · outbound

This paper cites Quantifying layer similarity in multiplex networks: A systematic study.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Quantifying layer similarity in multiplex networks: A systematic study

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.160408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 85fd5020-28d0-4486-95ed-57caea595477 · outbound

This paper cites Values of Non-Atomic Games; Princeton University Press: Princeton, NJ, USA, 1974.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach Values of Non-Atomic Games; Princeton University Press: Princeton, NJ, USA, 1974

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.150756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ed0f1b7b-f49f-444e-bdbe-c9a7fa6fdfca · outbound

This paper cites A unified approach to interpreting model predictions.

Identifying Key Nodes for the Influence Spread using a Machine Learning Approach A unified approach to interpreting model predictions

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:06:45.142257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.