A survey of dynamic graph neural networks

doi:10.1007/s11704-024-3853-2

Frontiers of Computer Science

2025, Vol. 19

Issue (6): 196323 https://doi.org/10.1007/s11704-024-3853-2

本期目录

A survey of dynamic graph neural networks

Yanping ZHENG, Lu YI, Zhewei WEI(

)

Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China

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Abstract：

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time. By integrating sequence modeling modules into traditional GNN architectures, dynamic GNNs aim to bridge this gap, capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models. We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated. We also discuss large-scale dynamic GNNs and pre-training techniques. Although dynamic GNNs have shown superior performance, challenges remain in scalability, handling heterogeneous information, and lack of diverse graph datasets. The paper also discusses possible future directions, such as adaptive and memory-enhanced models, inductive learning, and theoretical analysis.

Key words： graph neural networks dynamic graph temporal modeling large-scale

收稿日期: 2023-10-30 出版日期: 2024-09-11

Corresponding Author(s): Zhewei WEI

引用本文:

. [J]. Frontiers of Computer Science, 2025, 19(6): 196323.
Yanping ZHENG, Lu YI, Zhewei WEI. A survey of dynamic graph neural networks. Front. Comput. Sci., 2025, 19(6): 196323.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-024-3853-2
https://academic.hep.com.cn/fcs/CN/Y2025/V19/I6/196323

Fig.1

Notation	Description
$G$	The graph
$V$	The vertex set
$E$	The edge set
$n$	The number of nodes
$X ∈ R n × d$	The feature matrix
$x i ∈ R d$	The feature vector of node $i$
$A ∈ R n × n$	The adjacent matrix
$D ∈ R n × n$	The degree matrix
$P ∈ R n × n$	The normalized adjacent matrix
$L ∈ R n × n$	The Laplacian matrix
$σ$	The nonlinear activation function
$G$	The dynamic graph

Tab.1

Fig.2

Method	Models	Graph	Insertion		Deletion		Attribute
Method	Models	Graph	Node	Edge	Node	Edge	Node	Edge
WD-GCN [36]	GCN with skip connections and LSTM	DTDG	×	√	×	√	√	√
DySAT [37]	GAT and Transformer	DTDG	√	√	√	√	√	√
TNDCN [38]	GNNs and spatio-temporal convolutions	DTDG	×	√	×	√	√	√
TEDIC [39]	GNNs and spatio-temporal convolutions	DTDG	×	√	×	√	√	√
GC-LSTM [40]	GCN and LSTM	DTDG	×	√	×	√	√	√
LRGCN [41]	R-GCN and LSTM	DTDG	×	√	×	√	√	√
RE-Net [42]	integrate R-GCN within RNNs	DTDG	×	√	×	√	×	×
WinGNN [43]	GNN and randomized sliding-window	DTDG	√	√	√	√	□^*	×
ROLAND [4]	GNNs and RNNs	DTDG	√	√	√	√	√	√
EvolveGCN [44]	GCN and LSTM/GRU	DTDG	√	√	√	√	√	√
SEIGN [45]	GCN and LSTM/GRU	DTDG	√	√	√	√	√	√
dyngraph2vec [46]	AE and LSTM	DTDG	√	√	√	√	×	×
Know-Evolve [47]	RNN-parameterized TPP	CTDG	×	√	×	×	×	×
DyREP [48]	RNN-parameterized TPP	CTDG	√	√	×	×	×	×
LDG [49]	RNN-parameterized TPP and self attention	CTDG	√	√	×	×	×	×
GHNN [50]	LSTM-parameterized TPP	CTDG	×	√	×	×	×	×
GHT [51]	LSTM-parameterized TPP	CTDG	×	√	×	×	×	×
TREND [52]	Hawkes process-based GNN	CTDG	√	√	×	×	□^*	×
DGNN [53]	GNNs and LSTM	CTDG	√	√	×	×	×	×
JODIE [54]	GNNs and RNNs	CTDG	√	√	×	×	□^*	√
TGAT [2]	GAT, time encode and RNNs	CTDG	√	√	×	×	□^*	√
TGN [3]	GNNs, time encode and RNNs	CTDG	√	√	×	×	□^*	√
APAN [55]	GNNs and RNNs	CTDG	√	√	×	×	□^*	√
CAW-N [56]	causal anonymous walk based GNN	CTDG	√	√	×	×	×	√
Zebra [57]	temporal PPR based GNN	CTDG	√	√	×	×	√	√
EARLY [58]	GCN and temporal random walk	CTDG	√	√	√	√	√	√
Zheng et al. [59]	decoupled GNNs and sequential models	CTDG	√	√	√	√	√	√
SDG [60]	APPNP and dynamic propagation	CTDG	×	√	×	√	√	√

Tab.2

Fig.3

Tab.3

Types	Dataset	Domain	Category	$n$	$m$	$\| T \|$	$d e$
CTDG	Social Evolution [79]	Proximity	General	74	2,099,519	565,932	1^?
	Enron [80]	Social	General	184	125,235	22,632	?
	Contact [81]	Proximity	General	692	2,426,279	8,065	1^?
	UCI [82]	Social	General	1,899	59,835	58,911	100
	LastFM [54]	Interaction	Bipartite	1,980	1,293,103	1,283,614	?
	Bitcoin-Alpha [83,84]	Finance	General	3,783	24,186	24,186	?
	Bitcoin-OTC [83,84]	Finance	General	5,881	35,592	35,592	?
	MOOC [54]	Interaction	Bipartite	7,144	411,749	345,600	4^?
	Wikipedia [54]	Social	Bipartite	9,227	157,474	152,757	172
	Reddit [54]	Social	Bipartite	10,984	672,447	669,065	172
	GDELT [74,85]	Event	General	16,682	191,290,882	170,522	186
	eBay-Small [86]	E-commerce	Bipartite	38,427	384,677	?	?
	Taobao [87,88]	E-commerce	Bipartite	82,566	77,436	?	4^?
	YouTube-Reddit-Small [89]	Social	Bipartite	264,443	297,732	?	?
	eBay-Large [86]	E-commerce	Bipartite	1,333,594	1,119,454	?	?
	Taobao-Large [87,88]	E-commerce	Bipartite	1,630,453	5,008,745	?	4^?
	DGraphFin [90]	Social	General	3,700,550	4,300,999	?	?
	Youtube-Reddit-Large [89]	Social	Bipartite	5,724,111	4,228,523	?	?
DTDG	UN Vote [91]	Politics	General	201	1,035,742	72	1^?
	US Legislative [92,93]	Politics	General	225	60,396	12	1^?
	UN Trade [94]	Finance	General	255	507,497	32	1^?
	Canadian Parliament [92]	Politics	General	734	74,478	14	?
	Twitter-Tennis [95]	Social	General	1,000	40,839	120	?
	Autonomous systems [96]	Communication	General	7,716	13,895	733	?
	Flights [97]	Transport	General	13,169	1,927,145	122	1^?
	HEP-TH [96,98]	Citation	General	27,770	352,807	3,487	?
	Elliptic [99]	Finance	General	203,769	234,355	49	?
	MAG [11,74]	Citation	General	121,751,665	1,297,748,926	120	?

Tab.4

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