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Paper Citation Record · LEDGER

Graph signal aware decomposition of dynamic networks via latent graphs

As of 8 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2506.08519.

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

pith.paper-citation-record.v1
2506.08519 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:19:24.441764Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

49 of 49 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6154668-e5da-423b-b83c-75d9c71c1903 · outbound

This paper cites Tensor graph decomposition for temporal networks,.

Graph signal aware decomposition of dynamic networks via latent graphs Tensor graph decomposition for temporal networks,

Reference 1

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Observation 8458ac1c-31f2-4037-9584-21298c7cfc86 · outbound

This paper cites Temporal networks,.

Graph signal aware decomposition of dynamic networks via latent graphs Temporal networks,

Reference 2

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Observation a3c47801-9b20-4e36-ba71-9ebdb75afb8f · outbound

This paper cites Time- varying graphs and dynamic networks,.

Graph signal aware decomposition of dynamic networks via latent graphs Time- varying graphs and dynamic networks,

Reference 3

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Observation 4cea1c1e-8fe8-4f29-909e-7eb81c5c69b1 · outbound

This paper cites Network evolution by different rewiring schemes,.

Graph signal aware decomposition of dynamic networks via latent graphs Network evolution by different rewiring schemes,

Reference 4

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Observation 754be219-e772-457a-a96f-931c87330c0c · outbound

This paper cites Random graph models for dynamic networks,.

Graph signal aware decomposition of dynamic networks via latent graphs Random graph models for dynamic networks,

Reference 5

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Source-reported events for the cited work

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Observation 456f3b4f-c297-45a9-81ca-19102add5a51 · outbound

This paper cites Learning time varying graphs,.

Graph signal aware decomposition of dynamic networks via latent graphs Learning time varying graphs,

Reference 6

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Source-reported events for the cited work

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Observation 188249aa-5f23-44de-b633-422a83cce5d0 · outbound

This paper cites Learning time-varying graphs from online data,.

Graph signal aware decomposition of dynamic networks via latent graphs Learning time-varying graphs from online data,

Reference 7

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Source-reported events for the cited work

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Observation 74501997-6c94-4934-a79b-2f5f8d0f0f03 · outbound

This paper cites Sparse online learning with kernels using random features for estimating nonlinear dynamic graphs,.

Graph signal aware decomposition of dynamic networks via latent graphs Sparse online learning with kernels using random features for estimating nonlinear dynamic graphs,

Reference 8

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Source-reported events for the cited work

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Observation 4cb91c3c-23a8-4d5b-8446-0e8bd5e437b8 · outbound

This paper cites Online topology inference from streaming stationary graph signals with partial connectivity information,.

Graph signal aware decomposition of dynamic networks via latent graphs Online topology inference from streaming stationary graph signals with partial connectivity information,

Reference 9

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Source-reported events for the cited work

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Observation f5c2dfb9-95a5-4957-97b5-5649d7b5e3a9 · outbound

This paper cites Tensor decompositions for identifying directed graph topologies and tracking dynamic networks,.

Graph signal aware decomposition of dynamic networks via latent graphs Tensor decompositions for identifying directed graph topologies and tracking dynamic networks,

Reference 10

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Source-reported events for the cited work

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Observation efcc512b-6de3-4e1a-a72c-fba35d827bad · outbound

This paper cites Tracking switched dynamic network topologies from information cascades,.

Graph signal aware decomposition of dynamic networks via latent graphs Tracking switched dynamic network topologies from information cascades,

Reference 11

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Observation 0cf60428-eb67-4022-a1f4-ed37afb33385 · outbound

This paper cites Network inference via the time-varying graphical lasso,.

Graph signal aware decomposition of dynamic networks via latent graphs Network inference via the time-varying graphical lasso,

Reference 12

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Source-reported events for the cited work

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Observation e8a3a52a-fb16-44fb-abb0-e3518612755d · outbound

This paper cites Online network inference from graph-stationary signals with hidden nodes,.

Graph signal aware decomposition of dynamic networks via latent graphs Online network inference from graph-stationary signals with hidden nodes,

Reference 13

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Source-reported events for the cited work

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Observation 831cac82-b845-430a-af96-e519530d6abb · outbound

This paper cites Joint signal recovery and graph learning from incomplete time-series,.

Graph signal aware decomposition of dynamic networks via latent graphs Joint signal recovery and graph learning from incomplete time-series,

Reference 14

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Source-reported events for the cited work

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Observation 1ab78073-b9ea-4426-906d-03bc782aaea9 · outbound

This paper cites Multiview graph learning with consensus graph,.

Graph signal aware decomposition of dynamic networks via latent graphs Multiview graph learning with consensus graph,

Reference 15

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Source-reported events for the cited work

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Observation 8c8902a8-fa54-4a41-9a28-3cdbe17ff94c · outbound

This paper cites Online inference for mixture model of streaming graph signals with sparse excitation,.

Graph signal aware decomposition of dynamic networks via latent graphs Online inference for mixture model of streaming graph signals with sparse excitation,

Reference 16

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Observation af4e7159-2cdd-4620-8c10-638d384da658 · outbound

This paper cites Graph independent component analysis reveals repertoires of intrinsic network components in the human brain,.

Graph signal aware decomposition of dynamic networks via latent graphs Graph independent component analysis reveals repertoires of intrinsic network components in the human brain,

Reference 17

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Observation 0f629477-ee21-4b59-ab4a-5563e832f42f · outbound

This paper cites Independent component analysis: algorithms and applications,.

Graph signal aware decomposition of dynamic networks via latent graphs Independent component analysis: algorithms and applications,

Reference 18

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Observation b43d634f-50c8-447b-9151-a4174f3d2b84 · outbound

This paper cites Multi- layer network switching rate predicts brain performance,.

Graph signal aware decomposition of dynamic networks via latent graphs Multi- layer network switching rate predicts brain performance,

Reference 19

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Source-reported events for the cited work

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Observation 7bb05065-32f1-4431-882b-d7ffe15a7f3a · outbound

This paper cites Graph-adaptive semi- supervised tracking of dynamic processes over switching network modes,.

Graph signal aware decomposition of dynamic networks via latent graphs Graph-adaptive semi- supervised tracking of dynamic processes over switching network modes,

Reference 20

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Observation 883c03c9-2843-49e2-95e5-48e1941dfa32 · outbound

This paper cites Graph switching dynamical systems,.

Graph signal aware decomposition of dynamic networks via latent graphs Graph switching dynamical systems,

Reference 21

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Source-reported events for the cited work

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Observation e19acfa2-d48b-4551-8511-0a5fc9414445 · outbound

This paper cites Brain network adaptability across task states,.

Graph signal aware decomposition of dynamic networks via latent graphs Brain network adaptability across task states,

Reference 22

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Observation 5bcd029a-e94e-4d50-86f7-f99dbce3315c · outbound

This paper cites Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,.

Graph signal aware decomposition of dynamic networks via latent graphs Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,

Reference 23

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Observation 1de12a53-b0b5-44b4-a80d-e9416f7f5519 · outbound

This paper cites Tensor decompositions and applications,.

Graph signal aware decomposition of dynamic networks via latent graphs Tensor decompositions and applications,

Reference 24

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Source-reported events for the cited work

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Observation 782227d2-4acc-4911-bc7d-95ca0b205f6f · outbound

This paper cites A multilinear singular value decomposition,.

Graph signal aware decomposition of dynamic networks via latent graphs A multilinear singular value decomposition,

Reference 25

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Observation 471e0417-49ae-46e5-94a5-c48b19070f0b · outbound

This paper cites Decompositions of a higher-order tensor in block terms—part iii: Alternating least squares algorithms,.

Graph signal aware decomposition of dynamic networks via latent graphs Decompositions of a higher-order tensor in block terms—part iii: Alternating least squares algorithms,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7e7055c8-25af-44b2-a6b1-55f2b462f745 · outbound

This paper cites Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach,.

Graph signal aware decomposition of dynamic networks via latent graphs Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 3c0dc00c-3fff-4210-98e6-30bcb76df06d · outbound

This paper cites Larc: Learning activity-regularized overlapping communities across time,.

Graph signal aware decomposition of dynamic networks via latent graphs Larc: Learning activity-regularized overlapping communities across time,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2522cea0-3d1a-4124-80b2-a79d54361d5b · outbound

This paper cites Beyond rank-1: Discovering rich community structure in multi-aspect graphs,.

Graph signal aware decomposition of dynamic networks via latent graphs Beyond rank-1: Discovering rich community structure in multi-aspect graphs,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 95c19d3e-2516-4c2d-9c1e-1a5c3845c8dd · outbound

This paper cites Dynamic graph summa- rization: a tensor decomposition approach,.

Graph signal aware decomposition of dynamic networks via latent graphs Dynamic graph summa- rization: a tensor decomposition approach,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 00bf82fb-d300-4f50-8df7-4b41dac0ccb6 · outbound

This paper cites Temporal link prediction using matrix and tensor factorizations,.

Graph signal aware decomposition of dynamic networks via latent graphs Temporal link prediction using matrix and tensor factorizations,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8ae63a1e-7da1-43dd-903a-6e0f1ee3d29d · outbound

This paper cites Anomaly detection in temporal graph data: An iterative tensor decom- position and masking approach,.

Graph signal aware decomposition of dynamic networks via latent graphs Anomaly detection in temporal graph data: An iterative tensor decom- position and masking approach,

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation bc4e678e-fa36-4168-916d-840f0fb19b56 · outbound

This paper cites Identification of dynamic functional brain network states through tensor decomposition,.

Graph signal aware decomposition of dynamic networks via latent graphs Identification of dynamic functional brain network states through tensor decomposition,

Reference 33

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raw_fallback, observed 2026-08-07T05:19:24.851493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a8fe3b73-3769-429c-8bad-6377acbe923d · outbound

This paper cites A tensor decomposition-based approach for detecting dynamic network states from eeg,.

Graph signal aware decomposition of dynamic networks via latent graphs A tensor decomposition-based approach for detecting dynamic network states from eeg,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7978b604-aed2-4d13-8e5c-949f79195885 · outbound

This paper cites Recursive tensor subspace tracking for dynamic brain network analysis,.

Graph signal aware decomposition of dynamic networks via latent graphs Recursive tensor subspace tracking for dynamic brain network analysis,

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.820455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0c368ce4-9899-4463-8da8-b3411b20305a · outbound

This paper cites A generalized graph regularized non-negative tucker decomposition framework for tensor data representation,.

Graph signal aware decomposition of dynamic networks via latent graphs A generalized graph regularized non-negative tucker decomposition framework for tensor data representation,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.803765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.380543Z digest=sha256:5ad1fe9de13ac75bd81e9dae9f3d429eac0ab5c0016f0f0e322d946d9385cf70

Observation f51c92a1-2a59-4b6f-a94a-a650025d9309 · outbound

This paper cites Connecting the Dots: Identifying Network Structure via Graph Signal Processing,.

Graph signal aware decomposition of dynamic networks via latent graphs Connecting the Dots: Identifying Network Structure via Graph Signal Processing,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.786270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.385054Z digest=sha256:ddede5144dacc2855bb3722b3840c97688d80c5e7ff8b48b58c62a3db3db3868

Observation 9dc978c7-2f74-43c5-8da4-71f95b7b2a81 · outbound

This paper cites Learning graphs from smooth and graph-stationary signals with hidden variables,.

Graph signal aware decomposition of dynamic networks via latent graphs Learning graphs from smooth and graph-stationary signals with hidden variables,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.770361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.390395Z digest=sha256:7963ec10ead91b089e28cf98ca7c215e4f7dda4d4657a089368b3131330a913b

Observation 8a04038d-49d5-48ab-8d31-224f4fccd1b0 · outbound

This paper cites Joint network topology inference in the presence of hidden nodes,.

Graph signal aware decomposition of dynamic networks via latent graphs Joint network topology inference in the presence of hidden nodes,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.754140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.394788Z digest=sha256:a5dd64cc5995637823bdc8392968dbe906938cfa2e61b977615440583634faf9

Observation 21700e9c-5bfb-4e21-9142-441171aa71fe · outbound

This paper cites Privacy, social network sites, and social relations,.

Graph signal aware decomposition of dynamic networks via latent graphs Privacy, social network sites, and social relations,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.737943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.399494Z digest=sha256:cd422fcd0b6e296687a0e40d990b0d7143f67021ee666ad6496a87363768dbc8

Observation 320fab30-2272-41be-b153-aea22f9fbc17 · outbound

This paper cites Opportunities and challenges of wireless sensor networks in smart grid,.

Graph signal aware decomposition of dynamic networks via latent graphs Opportunities and challenges of wireless sensor networks in smart grid,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.722732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.403914Z digest=sha256:e7e01b0795624eaff4f71a73540d44de3c14ecdda7151015016e6a1dccb89864

Observation f95ec186-a200-4dcc-98a1-6691314ca295 · outbound

This paper cites Nonparametric bayesian learning of switching linear dynamical systems,.

Graph signal aware decomposition of dynamic networks via latent graphs Nonparametric bayesian learning of switching linear dynamical systems,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.707582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.409093Z digest=sha256:1fcd01c965e55c8b63836b0827be8bf04f59b8f4ecfbfe354b9739f3f62dfdaf

Observation a4d34400-bf82-41c6-b5db-012458922702 · outbound

This paper cites Learning laplacian matrix in smooth graph signal representations,.

Graph signal aware decomposition of dynamic networks via latent graphs Learning laplacian matrix in smooth graph signal representations,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.690735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.413261Z digest=sha256:79385afa3fa877fe6aa5c0da9c65e5b4fb230a78dc9d99d911b8617086b3f29f

Observation c54874e2-b026-4742-84f1-74051d3d1444 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers,.

Graph signal aware decomposition of dynamic networks via latent graphs Distributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.673076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.417637Z digest=sha256:720a1500f8c08ed5b1b787aea34933f5c6f419abb88a7d98656d09b83c5252eb

Observation 4ee7d972-fb38-4bdb-9d27-dfaf08deda1c · outbound

This paper cites Polynomial graphical lasso: Learning edges from gaussian graph-stationary signals,.

Graph signal aware decomposition of dynamic networks via latent graphs Polynomial graphical lasso: Learning edges from gaussian graph-stationary signals,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.653381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.422386Z digest=sha256:b66f28d9d05aef92eaf6ef619ceb005694c25e13377402c681fa40aeeff503f6

Observation b016d704-ea31-4f58-af7b-cc5001b2f0d0 · outbound

This paper cites A block coordinate descent method for regular- ized multiconvex optimization with applications to nonnegative tensor factorization and completion,.

Graph signal aware decomposition of dynamic networks via latent graphs A block coordinate descent method for regular- ized multiconvex optimization with applications to nonnegative tensor factorization and completion,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.636662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.427251Z digest=sha256:1331d2cfd0572b50b947f185a3e45f1464ddf83504a59089b477050a4fa7fb3d

Observation 5fd2e2a7-13de-48ff-b5de-bb5fd94d426c · outbound

This paper cites How to learn a graph from smooth signals,.

Graph signal aware decomposition of dynamic networks via latent graphs How to learn a graph from smooth signals,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.619325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.432331Z digest=sha256:1148ef0121c587f71f701184136d6e83dbe28ec9c1d564e1221c5e0143f5a83b

Observation 5b4aa88e-d8a9-4449-9499-731cdc6551c2 · outbound

This paper cites Reconstruction of time-varying graph signals via sobolev smoothness,.

Graph signal aware decomposition of dynamic networks via latent graphs Reconstruction of time-varying graph signals via sobolev smoothness,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.603502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.436767Z digest=sha256:59e20d713a07d3f88f8724bda0f5841757f91a152087f6b3397e8ba28eff51b0

Observation 41613663-dbe1-4e01-adda-4dbef622f484 · outbound

This paper cites Noaa’s 1981–2010 us climate normals: an overview,.

Graph signal aware decomposition of dynamic networks via latent graphs Noaa’s 1981–2010 us climate normals: an overview,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:19:24.586792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:19:24.441764Z digest=sha256:2cc22df9ab4be6bc62ecaff4bca6335493633e7262300f1fb4568a166de5807b

Pith citing papers

No inbound Pith citation observations are available.