Mining significant trend sequences in dynamic attributed graphs
P Fournier-Viger, C Cheng, Z Cheng, JCW Lin… - Knowledge-Based …, 2019 - Elsevier
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[PDF][PDF] Mining Significant Trend Sequences in Dynamic Attributed Graphs
P Fournier-Vigera, C Chengb, Z Chengc, JCW Lind… - philippe-fournier-viger.com
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[引用][C] Mining significant trend sequences in dynamic attributed graphs
P Fournier-Viger, C Cheng, Z Cheng, JCW Lin… - Knowledge-Based …, 2019 - hal.science
Mining significant trend sequences in dynamic attributed graphs - Archive ouverte HAL Accéder
directement au contenu Documentation FR Français (FR) Anglais (EN) Se connecter HAL …
directement au contenu Documentation FR Français (FR) Anglais (EN) Se connecter HAL …
[PDF][PDF] Mining Significant Trend Sequences in Dynamic Attributed Graphs
P Fournier-Vigera, C Chengb, Z Chengc, JCW Lind… - researchgate.net
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[PDF][PDF] Mining Significant Trend Sequences in Dynamic Attributed Graphs
P Fournier-Vigera, C Chengb, Z Chengc, JCW Lind… - academia.edu
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[引用][C] Mining significant trend sequences in dynamic attributed graphs
P Fournier-Viger, C Cheng, Z Cheng, JCW Lin… - Knowledge-Based …, 2019 - cir.nii.ac.jp
Mining significant trend sequences in dynamic attributed graphs | CiNii Research CiNii 国立
情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 論文・データをさがす …
情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 論文・データをさがす …
Mining significant trend sequences in dynamic attributed graphs
P Fournier-Viger, C Cheng, Z Cheng, JCW Lin… - 2019 - dl.acm.org
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[PDF][PDF] Mining Significant Trend Sequences in Dynamic Attributed Graphs
P Fournier-Vigera, C Chengb, Z Chengc, JCW Lind… - philippe-fournier-viger.com
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …
[引用][C] Mining significant trend sequences in dynamic attributed graphs
P Fournier-Viger, C Cheng, Z Cheng, JCW Lin… - Knowledge-Based …, 2019 - hal.science
Mining significant trend sequences in dynamic attributed graphs - Archive ouverte HAL Accéder
directement au contenu Documentation FR Français (FR) Anglais (EN) Se connecter HAL …
directement au contenu Documentation FR Français (FR) Anglais (EN) Se connecter HAL …
[PDF][PDF] Mining Significant Trend Sequences in Dynamic Attributed Graphs
P Fournier-Vigera, C Chengb, Z Chengc, JCW Lind… - philippe-fournier-viger.com
Discovering patterns in graphs has many applications such as social network, biological and
chemistry data analysis. Although many algorithms were proposed to identify interesting …
chemistry data analysis. Although many algorithms were proposed to identify interesting …