Reconstructing graphs and their connectivity using graphlets
https://arxiv.org/abs/2508.19189
Graphlets are small subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence, gives a topological description of the ...📈🤖
https://arxiv.org/abs/2508.19189
Graphlets are small subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence, gives a topological description of the ...📈🤖
August 27, 2025 at 4:56 PM
Reconstructing graphs and their connectivity using graphlets
https://arxiv.org/abs/2508.19189
Graphlets are small subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence, gives a topological description of the ...📈🤖
https://arxiv.org/abs/2508.19189
Graphlets are small subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence, gives a topological description of the ...📈🤖
Reconstructing graphs and their connectivity using graphlets
https://arxiv.org/abs/2508.19189
https://arxiv.org/abs/2508.19189
August 27, 2025 at 3:34 PM
Reconstructing graphs and their connectivity using graphlets
https://arxiv.org/abs/2508.19189
https://arxiv.org/abs/2508.19189
David Hartman, Aneta Pokorn\'a, Daniel Trlifaj: Reconstructing graphs and their connectivity using graphlets https://arxiv.org/abs/2508.19189 https://arxiv.org/pdf/2508.19189 https://arxiv.org/html/2508.19189
August 27, 2025 at 6:37 AM
David Hartman, Aneta Pokorn\'a, Daniel Trlifaj: Reconstructing graphs and their connectivity using graphlets https://arxiv.org/abs/2508.19189 https://arxiv.org/pdf/2508.19189 https://arxiv.org/html/2508.19189
Applied to five model organisms, it identified over-represented graphlets and orbits in oxidative stress response subnetworks, revealing conserved mixed interaction patterns.
August 6, 2025 at 10:30 AM
Applied to five model organisms, it identified over-represented graphlets and orbits in oxidative stress response subnetworks, revealing conserved mixed interaction patterns.
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
https://arxiv.org/abs/2505.12954
May 21, 2025 at 3:34 AM
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
https://arxiv.org/abs/2505.12954
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing w...📈🤖
https://arxiv.org/abs/2505.12954
The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing w...📈🤖
May 20, 2025 at 5:29 PM
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing w...📈🤖
https://arxiv.org/abs/2505.12954
The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing w...📈🤖
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
https://arxiv.org/abs/2505.12954
May 20, 2025 at 3:34 PM
Counting Graphlets of Size $k$ under Local Differential Privacy
https://arxiv.org/abs/2505.12954
https://arxiv.org/abs/2505.12954
$\Omega(n^{k - 1})$, demonstrating the optimality of our result. Furthermore, we establish that for certain input graphs and graphlets, any locally differentially private algorithm must have an expected $\ell_2$ error of $\Omega(n^{k - 1.5})$. Our [4/5 of https://arxiv.org/abs/2505.12954v1]
May 20, 2025 at 6:01 AM
$\Omega(n^{k - 1})$, demonstrating the optimality of our result. Furthermore, we establish that for certain input graphs and graphlets, any locally differentially private algorithm must have an expected $\ell_2$ error of $\Omega(n^{k - 1.5})$. Our [4/5 of https://arxiv.org/abs/2505.12954v1]
that the expected $\ell_2$ error of our algorithm is $O(n^{k - 1})$. Additionally, we prove that there exists a class of input graphs and graphlets of size $k$ for which any non-interactive counting algorithm incurs an expected $\ell_2$ error of [3/5 of https://arxiv.org/abs/2505.12954v1]
May 20, 2025 at 6:01 AM
that the expected $\ell_2$ error of our algorithm is $O(n^{k - 1})$. Additionally, we prove that there exists a class of input graphs and graphlets of size $k$ for which any non-interactive counting algorithm incurs an expected $\ell_2$ error of [3/5 of https://arxiv.org/abs/2505.12954v1]
focuses on small graphlets like triangles or $k$-stars. In this paper, we propose a non-interactive, locally differentially private algorithm capable of counting graphlets of any size $k$. When $n$ is the number of nodes in the input graph, we show [2/5 of https://arxiv.org/abs/2505.12954v1]
May 20, 2025 at 6:01 AM
focuses on small graphlets like triangles or $k$-stars. In this paper, we propose a non-interactive, locally differentially private algorithm capable of counting graphlets of any size $k$. When $n$ is the number of nodes in the input graph, we show [2/5 of https://arxiv.org/abs/2505.12954v1]
arXiv:2505.12954v1 Announce Type: new
Abstract: The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work [1/5 of https://arxiv.org/abs/2505.12954v1]
Abstract: The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work [1/5 of https://arxiv.org/abs/2505.12954v1]
May 20, 2025 at 6:01 AM
arXiv:2505.12954v1 Announce Type: new
Abstract: The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work [1/5 of https://arxiv.org/abs/2505.12954v1]
Abstract: The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work [1/5 of https://arxiv.org/abs/2505.12954v1]
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand: Counting Graphlets of Size $k$ under Local Differential Privacy https://arxiv.org/abs/2505.12954 https://arxiv.org/pdf/2505.12954 https://arxiv.org/html/2505.12954
May 20, 2025 at 6:01 AM
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand: Counting Graphlets of Size $k$ under Local Differential Privacy https://arxiv.org/abs/2505.12954 https://arxiv.org/pdf/2505.12954 https://arxiv.org/html/2505.12954
Counting Graphlets of Size $k$ under Local Differential Privacy
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand
http://arxiv.org/abs/2505.12954
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand
http://arxiv.org/abs/2505.12954
May 20, 2025 at 3:48 AM
Counting Graphlets of Size $k$ under Local Differential Privacy
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand
http://arxiv.org/abs/2505.12954
Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand
http://arxiv.org/abs/2505.12954
relationships in a dynamic network. Specifically, to define structural equivalence in a dynamic network, we use temporal subgraphs, known as dynamic graphlets, to capture how a node's neighborhood structure evolves over time. We then introduce a [5/7 of https://arxiv.org/abs/2503.19926v1]
March 27, 2025 at 6:00 AM
relationships in a dynamic network. Specifically, to define structural equivalence in a dynamic network, we use temporal subgraphs, known as dynamic graphlets, to capture how a node's neighborhood structure evolves over time. We then introduce a [5/7 of https://arxiv.org/abs/2503.19926v1]
Please, my \hbox, he is very overfull
people.eecs.berkeley.edu/~matei/paper...
#graphlets #TeX #typography
people.eecs.berkeley.edu/~matei/paper...
#graphlets #TeX #typography
March 10, 2025 at 1:35 PM
Please, my \hbox, he is very overfull
people.eecs.berkeley.edu/~matei/paper...
#graphlets #TeX #typography
people.eecs.berkeley.edu/~matei/paper...
#graphlets #TeX #typography
We are joined by Prof. Nataša Pržulj, creator of #graphlets and current algorithm designer for multi-omics data fusion! She has recently become a full-time professor of Computational Biology at @mbzuai.bsky.social, check her work here:
life.bsc.es/iconbi/
life.bsc.es/iconbi/
February 28, 2025 at 3:17 PM
We are joined by Prof. Nataša Pržulj, creator of #graphlets and current algorithm designer for multi-omics data fusion! She has recently become a full-time professor of Computational Biology at @mbzuai.bsky.social, check her work here:
life.bsc.es/iconbi/
life.bsc.es/iconbi/
Joshua Collyer, Tim Watson, Iain Phillips
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
November 13, 2024 at 6:30 AM
Joshua Collyer, Tim Watson, Iain Phillips
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Joshua Collyer, Tim Watson, Iain Phillips
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
June 6, 2024 at 4:01 AM
Joshua Collyer, Tim Watson, Iain Phillips
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets
https://arxiv.org/abs/2406.02606
Graphlets correct for the topological information missed by random walks
https://arxiv.org/abs/2405.14194
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space,...📈🤖
https://arxiv.org/abs/2405.14194
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space,...📈🤖
May 24, 2024 at 4:38 PM
Graphlets correct for the topological information missed by random walks
https://arxiv.org/abs/2405.14194
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space,...📈🤖
https://arxiv.org/abs/2405.14194
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space,...📈🤖
Graphlets correct for the topological information missed by random walks
https://arxiv.org/abs/2405.14194
https://arxiv.org/abs/2405.14194
May 24, 2024 at 3:34 PM
Graphlets correct for the topological information missed by random walks
https://arxiv.org/abs/2405.14194
https://arxiv.org/abs/2405.14194
Alessio Conte, Roberto Grossi, Yasuaki Kobayashi, Kazuhiro Kurita, Davide Rucci, Takeaki Uno, Kunihiro Wasa
Enumerating Graphlets with Amortized Time Complexity Independent of Graph Size
https://arxiv.org/abs/2405.13613
Enumerating Graphlets with Amortized Time Complexity Independent of Graph Size
https://arxiv.org/abs/2405.13613
May 24, 2024 at 4:01 AM
Alessio Conte, Roberto Grossi, Yasuaki Kobayashi, Kazuhiro Kurita, Davide Rucci, Takeaki Uno, Kunihiro Wasa
Enumerating Graphlets with Amortized Time Complexity Independent of Graph Size
https://arxiv.org/abs/2405.13613
Enumerating Graphlets with Amortized Time Complexity Independent of Graph Size
https://arxiv.org/abs/2405.13613