Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds | Paper Citation Record

Observation f7cd8396-e57a-4af1-a07f-a3086c2929b2 · outbound

This paper cites De-anonymizing social networks,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds De-anonymizing social networks,

Reference 1

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Observation 9537a2c5-efd8-4500-965b-f6693c84e9cf · outbound

This paper cites An efficient reconciliation algorithm for social networks,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds An efficient reconciliation algorithm for social networks,

Reference 2

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Observation f963d1d2-4705-45d9-b1dc-95a49622ea66 · outbound

This paper cites Pairwise global alignment of protein interaction networks by matching neighborhood topology,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Pairwise global alignment of protein interaction networks by matching neighborhood topology,

Reference 3

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This paper cites Robust textual inference via graph matching,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Robust textual inference via graph matching,

Reference 4

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This paper cites On the privacy of anonymized networks,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds On the privacy of anonymized networks,

Reference 5

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Observation 0855e7c8-c6e6-404a-b9a9-4b7cc23d3fc4 · outbound

This paper cites Improved achievability and converse bounds for Erd ˝os-R´enyi graph matching,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Improved achievability and converse bounds for Erd ˝os-R´enyi graph matching,

Reference 6

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Observation 2ea68cce-d6b6-4298-9372-ce3b71be9d72 · outbound

This paper cites Exact alignment recovery for correlated Erd\H{o}s-R\'enyi graphs.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Exact alignment recovery for correlated Erd\H{o}s-R\'enyi graphs

Reference 7

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Observation 197267ff-c21e-469c-8ad8-f23305e621d9 · outbound

This paper cites Settling the sharp reconstruction thresholds of random graph matching,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Settling the sharp reconstruction thresholds of random graph matching,

Reference 8

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Observation 58d5df47-1ba1-404a-a813-827c87d242e7 · outbound

This paper cites Matching recovery threshold for correlated random graphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Matching recovery threshold for correlated random graphs,

Reference 9

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This paper cites The number of trees,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds The number of trees,

Reference 10

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Observation 86bc338b-83cc-45ec-a866-6a7adbd59b0d · outbound

This paper cites Random graph matching at otter’s threshold via counting chandeliers,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Random graph matching at otter’s threshold via counting chandeliers,

Reference 11

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Observation 31889f00-333b-4c0a-a2cb-4a9a95d82958 · outbound

This paper cites Correlation detection in trees for planted graph alignment,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Correlation detection in trees for planted graph alignment,

Reference 12

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Observation f1ca7ba0-24ea-4862-bae5-8218934d1cde · outbound

This paper cites Statistical limits of correlation detection in trees,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Statistical limits of correlation detection in trees,

Reference 13

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Observation 473902f5-233f-4236-8794-d58321c56d84 · outbound

This paper cites Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Algorithmic contiguity from low-degree conjecture and applications in correlated random graphs,

Reference 14

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Observation b809e87e-e019-4e92-83d1-b4bb5ee76d92 · outbound

This paper cites Kempe, J.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Kempe, J

Reference 15

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Observation a920a416-5a61-4709-b3b0-2cae857faf31 · outbound

This paper cites Asymmetric graph alignment and the phase transition for asymmetric tree correlation testing,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Asymmetric graph alignment and the phase transition for asymmetric tree correlation testing,

Reference 16

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Observation 0e777308-2b76-4543-b1b3-b92e5f652c13 · outbound

This paper cites Ix. on the problem of the most efficient tests of statistical hypotheses,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Ix. on the problem of the most efficient tests of statistical hypotheses,

Reference 17

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This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 18

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Observation 7c4e0524-c0a1-43a0-8f20-3111dbd3ea05 · outbound

This paper cites Frieze and M.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Frieze and M

Reference 19

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Observation 9be7fbd5-1b1c-474d-aae7-0285a5d7d31f · outbound

This paper cites Optimal recovery of correlated Erd\H{o}s-R\'enyi graphs.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Optimal recovery of correlated Erd\H{o}s-R\'enyi graphs

Reference 20

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Observation a14eca3a-3bdf-4ed6-9e2f-91e6809ec1ee · outbound

This paper cites Partial recovery of erd ˝os-r´enyi graph alignment via k-core alignment,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Partial recovery of erd ˝os-r´enyi graph alignment via k-core alignment,

Reference 21

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Observation 867c7ad8-2904-4fff-a6cd-e9d1712e2a9e · outbound

This paper cites Impossibility of partial recovery in the graph alignment problem,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Impossibility of partial recovery in the graph alignment problem,

Reference 22

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Observation 745ece4a-687a-435b-9746-af010c222637 · outbound

This paper cites Partial recovery in the graph alignment problem,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Partial recovery in the graph alignment problem,

Reference 23

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Observation 545d457f-1a71-4612-9168-5f2fbde249e8 · outbound

This paper cites Spectral graph matching and regularized quadratic relaxations: Algorithm and theory,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Spectral graph matching and regularized quadratic relaxations: Algorithm and theory,

Reference 24

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Observation a7efc77d-95c9-4a74-b1a6-19a4aff31479 · outbound

This paper cites Analysis of a canonical labeling algorithm for the alignment of correlated erd ˝os-r´enyi graphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Analysis of a canonical labeling algorithm for the alignment of correlated erd ˝os-r´enyi graphs,

Reference 25

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Observation 85982a96-ff87-4099-b12e-4542dde0a188 · outbound

This paper cites Exact matching of random graphs with constant correlation,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Exact matching of random graphs with constant correlation,

Reference 26

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Observation 1b5745cd-7793-444c-b094-d300499a4899 · outbound

This paper cites A polynomial-time iterative algorithm for random graph matching with nonvanishing correlation,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds A polynomial-time iterative algorithm for random graph matching with nonvanishing correlation,

Reference 27

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This paper cites Exact community recovery in correlated stochastic block models,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Exact community recovery in correlated stochastic block models,

Reference 28

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Observation 4d5dc9d4-4673-46ab-a62d-17169b6f5eba · outbound

This paper cites Efficient graph matching for correlated stochastic block models,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Efficient graph matching for correlated stochastic block models,

Reference 29

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Observation b9795a4b-e697-4254-990d-d00b40f315d8 · outbound

This paper cites Attributed graph alignment,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Attributed graph alignment,

Reference 30

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Observation 27f4296e-092a-4c07-afd0-64a57b7c1195 · outbound

This paper cites On the feasible region of efficient algorithms for attributed graph alignment,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds On the feasible region of efficient algorithms for attributed graph alignment,

Reference 31

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Observation 000d931f-c460-4371-9334-799f3e8e36bf · outbound

This paper cites Efficient algorithms for attributed graph alignment with vanishing edge correlation,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Efficient algorithms for attributed graph alignment with vanishing edge correlation,

Reference 32

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Observation 494ef5b9-3634-4ac9-81ea-4440f29fe65a · outbound

This paper cites Exact graph matching in correlated gaussian-attributed erd ˝os-r´enyi model,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Exact graph matching in correlated gaussian-attributed erd ˝os-r´enyi model,

Reference 33

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Observation ad1cc5ab-21a4-4239-b57a-e450749a705f · outbound

This paper cites Information-theoretic thresholds for the alignments of partially correlated graphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Information-theoretic thresholds for the alignments of partially correlated graphs,

Reference 34

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Observation 70daf101-212d-4779-a316-1a7716ed7340 · outbound

This paper cites Aligning Multiple Inhomogeneous Random Graphs: Fundamental Limits of Exact Recovery.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Aligning Multiple Inhomogeneous Random Graphs: Fundamental Limits of Exact Recovery

Reference 35

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Observation 2403c0d1-55c0-4a9c-baab-f5ca8d1f4ec6 · outbound

This paper cites Taming verification hardness: an efficient algorithm for testing subgraph isomorphism,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Taming verification hardness: an efficient algorithm for testing subgraph isomorphism,

Reference 36

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This paper cites Graphs-at-a-time: query language and access methods for graph databases,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Graphs-at-a-time: query language and access methods for graph databases,

Reference 37

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This paper cites On the information-theoretic limit of subgraph alignment,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds On the information-theoretic limit of subgraph alignment,

Reference 38

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This paper cites Cliques in random graphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Cliques in random graphs,

Reference 39

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Observation 271d512b-e85e-4bae-bb96-6f9939ba34c9 · outbound

This paper cites Finding a large hidden clique in a random graph,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Finding a large hidden clique in a random graph,

Reference 40

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Observation 14df9640-13df-473d-a45a-62b9c5e2200e · outbound

This paper cites Sharp thresholds in inference of planted subgraphs,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Sharp thresholds in inference of planted subgraphs,

Reference 41

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Observation 5e4a6074-ad19-4b49-a18b-1b9e7b1d09a6 · outbound

This paper cites The fundamental limits of recovering planted subgraphs (extended abstract),.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds The fundamental limits of recovering planted subgraphs (extended abstract),

Reference 42

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Observation 6f41fbcf-1f4c-4ed3-9d53-c6d9b2edb988 · outbound

This paper cites A lower bound for the critical probability in a certain percolation process,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds A lower bound for the critical probability in a certain percolation process,

Reference 43

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Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 44

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Observation 0ac5b177-57ff-4bdb-afb6-bf556042e3e7 · outbound

This paper cites Probability inequalities for the sum of independent random variables,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Probability inequalities for the sum of independent random variables,

Reference 45

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Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 46

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Observation f22410c6-2e34-4b3d-87a4-e3a591328b18 · outbound

This paper cites Reconstruction and estimation in the planted partition model,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Reconstruction and estimation in the planted partition model,

Reference 47

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Observation 1b736cb3-4da4-497c-9831-4511a0daf2b1 · outbound

This paper cites The poisson approximation to the poisson binomial distribution,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds The poisson approximation to the poisson binomial distribution,

Reference 48

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Observation 23120689-0a78-4952-82e8-162d1f494a14 · outbound

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Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 49

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Observation 73695e8f-042d-4487-bced-f7d9682a7e88 · outbound

This paper cites Moreover, the canonical order of vertices at each depth in the two trees are defined in the same way.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Moreover, the canonical order of vertices at each depth in the two trees are defined in the same way

Reference 50

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Observation 1ccb282d-b29b-40e8-ba10-3d7c03e0bf10 · outbound

This paper cites ,˜z˜t)of length ˜t∈[l]withz 1 =j 1 andz ˜t =xin graph ˜G2.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds ,˜z˜t)of length ˜t∈[l]withz 1 =j 1 andz ˜t =xin graph ˜G2

Reference 51

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Observation f28b736d-f158-4aa7-92c0-f5e5251c0b5b · outbound

This paper cites , d}and depthd∈[d max], P(E c 1 |v∈V d) =O(n −ϵ/3).(15) Lemma 4.Supposeλsq >1.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds , d}and depthd∈[d max], P(E c 1 |v∈V d) =O(n −ϵ/3).(15) Lemma 4.Supposeλsq >1

Reference 52

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Observation 282ecd47-c08c-4014-9c3e-312ebd510bd5 · outbound

This paper cites , d}and depthd∈[d max], P(E c 2 |v∈V d) = ˜O(n−1/3).(17) We prove these two propositions in the next two subsections respectively.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds , d}and depthd∈[d max], P(E c 2 |v∈V d) = ˜O(n−1/3).(17) We prove these two propositions in the next two subsections respectively

Reference 53

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Observation 7528f567-fa2f-41d1-8356-2cc49a197ac0 · outbound

This paper cites By Lemma 1, we know thatV ≤d =N G′ 1(1, d)for eachd≥0.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds By Lemma 1, we know thatV ≤d =N G′ 1(1, d)for eachd≥0

Reference 54

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Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 55

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Observation c615a8ac-4792-4cb2-ac1e-6c19f9135bea · outbound

This paper cites , vd)and 26 p′ = (v′ 0, v′ 1,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds , vd)and 26 p′ = (v′ 0, v′ 1,

Reference 56

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This paper cites With the definitions of these two collections and their corresponding total ordering, we have the following lemma.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds With the definitions of these two collections and their corresponding total ordering, we have the following lemma

Reference 57

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Observation 5c8fee59-a8ee-408c-891f-c4748b401992 · outbound

This paper cites ThenH p,p′ must satisfy the following two properties: 1)H p,p′ is a tree.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds ThenH p,p′ must satisfy the following two properties: 1)H p,p′ is a tree

Reference 58

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Observation 6a2782f8-ca4a-47ef-897a-53bbe9d1bc2a · outbound

This paper cites Proof of Lemma 5.To prove the lemma, we consider two separate case:k=dandk̸=d.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Proof of Lemma 5.To prove the lemma, we consider two separate case:k=dandk̸=d

Reference 59

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Observation ae66467a-91a9-48f4-b1cf-27d786af8bdc · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 60

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Observation 28e0a97d-f090-4926-b4d6-1c7057a35022 · outbound

This paper cites Then we have P(the2 p logn-neighborhood ofiinGcontains a cycle| H ⊂ G) =O(n −1+γ)(23) for any constantγ >0.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Then we have P(the2 p logn-neighborhood ofiinGcontains a cycle| H ⊂ G) =O(n −1+γ)(23) for any constantγ >0

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 8b03a6bb-c7b9-40a0-b4b2-3ee40ad972c7 · outbound

This paper cites , d max}, we define event Ak ={|N G′ 1(1, k)| ≤K(λq) k logn} and E1,k ={∄i∈[n] :i∈S G′ 1(1, k)and the2l-neighborhood of ¯iin ¯Gcontains a cycle}.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds , d max}, we define event Ak ={|N G′ 1(1, k)| ≤K(λq) k logn} and E1,k ={∄i∈[n] :i∈S G′ 1(1, k)and the2l-neighborhood of ¯iin ¯Gcontains a cycle}

Reference 62

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Observation 798988ba-2a9d-4856-b762-f5ca7e3486e7 · outbound

This paper cites It now suffices to show that for eachk∈ {0,.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds It now suffices to show that for eachk∈ {0,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 268351c3-9eeb-4ac1-8103-9c4d669121a7 · outbound

This paper cites To simplify the notation, we useθto denote the likelihood thresholdexp (λsq)l−1 logn in this proof.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds To simplify the notation, we useθto denote the likelihood thresholdexp (λsq)l−1 logn in this proof

Reference 64

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Observation 7dc429bc-bab9-45ee-b924-755d73ebc08f · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 45b3bf1d-86aa-4496-9055-dc2768381be8 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 27d4e3d4-f671-4788-8a22-75fc001b69b0 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 67

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Observation 3bcf99f1-75ed-4602-92bd-7e4935eef7f1 · outbound

This paper cites We have P(E c 2 |V d ̸=∅)≥P(E c 2 ∩ {v∈V d} |V d ̸=∅) =P(v∈V d |V d ̸=∅)·P(E c 2 |v∈V d) ≥ 1 n ·P(E c 2 |v∈V d), where the last inequality follows by symmetry.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds We have P(E c 2 |V d ̸=∅)≥P(E c 2 ∩ {v∈V d} |V d ̸=∅) =P(v∈V d |V d ̸=∅)·P(E c 2 |v∈V d) ≥ 1 n ·P(E c 2 |v∈V d), where the last inequality follows by symmetry

Reference 68

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Observation 8a6462f8-f293-47dc-bf8c-7aa2813bfe3f · outbound

This paper cites Therefore, to complete the proof of (17), it suffices to show thatP(V d ̸=∅) = Θ(1).

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Therefore, to complete the proof of (17), it suffices to show thatP(V d ̸=∅) = Θ(1)

Reference 69

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Observation aa5ab3ef-838c-480b-bbc2-8faccdbab107 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

Source-reported events for the cited work

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Observation 25fe7a78-9616-450e-b29e-cf6bfa67eed2 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 71

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Observation 582d9813-9191-41c4-a172-66b9c008900f · outbound

This paper cites This algorithm starts at depthdof both trees.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds This algorithm starts at depthdof both trees

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 88cad733-e92f-45ba-97ff-b7147847240a · outbound

This paper cites 3)H=T IC v,dmax−d+l, whereT IC v,dmax−d+l is the subtree inT IC rooted atvwith depth up tod max −d+l.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds 3)H=T IC v,dmax−d+l, whereT IC v,dmax−d+l is the subtree inT IC rooted atvwith depth up tod max −d+l

Reference 73

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Observation 4352784f-4cfd-4acf-943f-656cb311401a · outbound

This paper cites LetA={|N ↑| ≤ n1−2ϵ/3}.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds LetA={|N ↑| ≤ n1−2ϵ/3}

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Observation 967148c1-43ff-44d0-a8c2-dad8f1e221b1 · outbound

This paper cites Notice thatd max −d+l=o(logn).

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Notice thatd max −d+l=o(logn)

Reference 75

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Observation f1f1c2f7-f4f6-4205-ad23-bb975b61ca08 · outbound

This paper cites DefineS=∪ i∈Vd\{v}Si.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds DefineS=∪ i∈Vd\{v}Si

Reference 76

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

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Unavailable: canonical work link unavailable.

Observation b94533a5-c42c-408a-a40a-47882196d624 · outbound

This paper cites 3)H=T IC v,l+l+, whereT IC i,l+l+ is the subtree inT IC rooted atiwith depth up tod max −d+l.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds 3)H=T IC v,l+l+, whereT IC i,l+l+ is the subtree inT IC rooted atiwith depth up tod max −d+l

Reference 77

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Observation b46bcc76-284d-49dc-829d-4060d13b5132 · outbound

This paper cites 48 Since we want to find a coupling under whichP(H ∼= T, ˜H ∼= ˜T)≥1− ˜O(n−3γ/4).

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds 48 Since we want to find a coupling under whichP(H ∼= T, ˜H ∼= ˜T)≥1− ˜O(n−3γ/4)

Reference 78

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13737a04-2907-4f45-aa87-31de6ba77a5a · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-06-27T05:46:05.715937Z

Source-reported events for the cited work

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Observation 18608611-2374-4869-a111-c933280ed32a · outbound

This paper cites Proof of Lemma 9.Notice that under eventsE k−1,A c k andB c k, bothH k and ˜Hk are trees.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Proof of Lemma 9.Notice that under eventsE k−1,A c k andB c k, bothH k and ˜Hk are trees

Reference 80

Resolution: malformed identifier; no resolver link, observed 2026-06-27T05:46:05.715937Z

Source-reported events for the cited work

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Observation a1f41da7-e292-4b19-8529-8ad6855b2836 · outbound

This paper cites Then we have P(the2 p logn-neighborhood ofiinGcontains a cycle| H ⊂ G) =O(n −1+γ)(23) for any constantγ >0.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Then we have P(the2 p logn-neighborhood ofiinGcontains a cycle| H ⊂ G) =O(n −1+γ)(23) for any constantγ >0

Reference 81

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Observation e763a5d5-8429-405d-81a1-54faf82935bf · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 82

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7890f28-52f9-4c1d-9d9a-07ef072a4a7f · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 83

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Observation 481b9007-0b3f-4098-b7d4-37afe24acc47 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 84

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Source-reported events for the cited work

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Observation db2f0a98-a7cb-4491-9a3f-c6750cbbc3bf · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 85

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Observation 43284a54-ab43-4e15-9435-0bba7118b4d5 · outbound

This paper cites an unresolved cited work.

Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Unresolved cited work

Reference 86

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