Repository navigation
Expand file tree
/
Copy pathindex.html
More file actions
210 lines (210 loc) · 26 KB
/
Copy pathindex.html
File metadata and controls
210 lines (210 loc) · 26 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Zhixiang Shen is a Computer Science Ph.D. student at the University of Illinois Chicago. Research in AI agents, large language models, and graph foundation models.">
<meta name="theme-color" content="#2457b3">
<meta property="og:title" content="Zhixiang Shen | UIC Computer Science">
<meta property="og:description" content="Research, publications, and academic activities in AI agents and graph machine learning.">
<meta property="og:type" content="website">
<meta property="og:url" content="https://zxlearningdeep.github.io/">
<meta property="og:image" content="https://zxlearningdeep.github.io/assets/profile.jpg">
<title>Zhixiang Shen | UIC Computer Science</title>
<link rel="icon" href="assets/favicon.svg" type="image/svg+xml">
<link rel="stylesheet" href="style.css">
<script src="script.js" defer></script>
</head>
<body>
<a class="skip-link" href="#main">Skip to content</a>
<svg class="icon-definitions" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><symbol id="icon-arrow" viewBox="0 0 24 24"><path d="M7 17 17 7M7 7h10v10"/></symbol><symbol id="icon-pin" viewBox="0 0 24 24"><path d="M20 10c0 6-8 12-8 12S4 16 4 10a8 8 0 1 1 16 0Z"/><circle cx="12" cy="10" r="2.5"/></symbol><symbol id="icon-mail" viewBox="0 0 24 24"><rect x="3" y="5" width="18" height="14" rx="2"/><path d="m3 6 9 7 9-7"/></symbol><symbol id="icon-scholar" viewBox="0 0 24 24"><path d="m2 9 10-5 10 5-10 5L2 9Zm4 2v6c3 3 9 3 12 0v-6M22 9v7"/></symbol><symbol id="icon-github" viewBox="0 0 24 24"><path d="M9 19c-4 1-4-2-6-2m12 5v-4c0-1 .1-2-.8-3 3.1-.3 6.3-1.5 6.3-6.8 0-1.5-.5-2.7-1.4-3.7.2-.4.6-1.8-.2-3.5 0 0-1.2-.4-3.9 1.4a13.5 13.5 0 0 0-7 0C5.3.6 4.1 1 4.1 1c-.8 1.7-.4 3.1-.2 3.5C3 5.5 2.5 6.7 2.5 8.2c0 5.3 3.2 6.5 6.3 6.8-.8.8-.8 1.6-.8 3v4"/></symbol><symbol id="icon-file" viewBox="0 0 24 24"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8l-6-6Zm0 0v6h6M8 13h8M8 17h5"/></symbol><symbol id="icon-code" viewBox="0 0 24 24"><path d="m8 5-7 7 7 7m8-14 7 7-7 7M14 3l-4 18"/></symbol><symbol id="icon-quote" viewBox="0 0 24 24"><path d="M3 12h6v7H3v-7Zm12 0h6v7h-6v-7ZM3 12V9a4 4 0 0 1 4-4m8 7V9a4 4 0 0 1 4-4"/></symbol><symbol id="icon-expand" viewBox="0 0 24 24"><path d="M9 3H3v6m12-6h6v6M3 15v6h6m12-6v6h-6"/></symbol><symbol id="icon-close" viewBox="0 0 24 24"><path d="m6 6 12 12M6 18 18 6"/></symbol><symbol id="icon-copy" viewBox="0 0 24 24"><rect x="9" y="9" width="12" height="12" rx="2"/><path d="M5 15H3V3h12v2"/></symbol></svg>
<header class="site-header">
<div class="header-inner">
<a class="brand" href="#about" aria-label="Zhixiang Shen homepage"><span class="brand-mark">ZS<span></span></span><span>Zhixiang Shen</span></a>
<nav class="main-nav" aria-label="Main navigation">
<a href="#about" class="active" aria-current="location">About</a><a href="#news">News</a><a href="#publications">Publications</a><a href="#education">Education</a><a href="#services">Service</a>
</nav>
</div>
</header>
<div class="page">
<aside class="sidebar" aria-label="Profile and contact information">
<div class="profile-card">
<img class="profile-photo" src="assets/profile.jpg" width="602" height="602" alt="Portrait of Zhixiang Shen">
<h1>Zhixiang Shen</h1>
<p class="profile-role">Ph.D. Student</p><p class="profile-department">Computer Science</p>
<a class="profile-school" href="https://www.uic.edu/" target="_blank" rel="noopener noreferrer" >University of Illinois Chicago</a>
<div class="profile-location"><svg class="icon " aria-hidden="true"><use href="#icon-pin"/></svg>Chicago, Illinois</div>
<div class="contact-block"><span class="contact-label">GET IN TOUCH</span><p><svg class="icon " aria-hidden="true"><use href="#icon-mail"/></svg><span>zshen33 [at] uic [dot] edu</span></p><p><svg class="icon " aria-hidden="true"><use href="#icon-mail"/></svg><span>zhixiang.zxs [at]<br> gmail [dot] com</span></p></div>
<div class="social-links"><a class="" href="https://scholar.google.com/citations?user=W_fgHnwAAAAJ&hl=en" target="_blank" rel="noopener noreferrer" ><svg class="icon " aria-hidden="true"><use href="#icon-scholar"/></svg>Google Scholar<svg class="icon small" aria-hidden="true"><use href="#icon-arrow"/></svg></a><a class="" href="https://github.com/zxlearningdeep" target="_blank" rel="noopener noreferrer" ><svg class="icon " aria-hidden="true"><use href="#icon-github"/></svg>GitHub<svg class="icon small" aria-hidden="true"><use href="#icon-arrow"/></svg></a></div>
</div>
<p class="sidebar-note">AI agents & graph machine learning</p>
</aside>
<main class="content" id="main">
<section id="about" class="about-section" aria-labelledby="about-title">
<p class="eyebrow">UNIVERSITY OF ILLINOIS CHICAGO</p>
<h2 id="about-title">About Me<span class="heading-dot">.</span></h2>
<p>Hi! I’m <strong>Zhixiang Shen</strong>, a Ph.D. student in Computer Science at the <a class="" href="https://www.uic.edu/" target="_blank" rel="noopener noreferrer" >University of Illinois Chicago</a>, advised by Prof. <a class="" href="https://cs.uic.edu/profiles/philip-yu/" target="_blank" rel="noopener noreferrer" >Philip S. Yu</a>.</p>
<p>My research interests include <strong>AI agents, large language models, and graph foundation models</strong>. I am interested in learning systems that reason over structured information and interact with complex environments. My previous work explores graph structure learning, heterogeneous graphs, and multi-domain graph learning.</p>
<p>I received my B.Eng. in Computer Science from the <a class="" href="https://en.uestc.edu.cn/" target="_blank" rel="noopener noreferrer" >University of Electronic Science and Technology of China</a> (UESTC, 2021–2025), where I worked closely with Prof. <a class="" href="https://sites.google.com/site/zhaokanghomepage/" target="_blank" rel="noopener noreferrer" >Zhao Kang</a>.</p>
<div class="research-interests" aria-label="Research interests"><span>AI Agents</span><span>Large Language Models</span><span>Graph Foundation Models</span><span>Graph Structure Learning</span></div>
</section>
<section id="news" class="news-section" aria-labelledby="news-title">
<div class="section-heading"><h2 id="news-title">News</h2><span class="section-note">Recent updates</span></div>
<div class="news-panel">
<div class="news-item"><time datetime="2026-08">Aug 2026</time><p>I started my Ph.D. in Computer Science at <strong>UIC</strong>, advised by Prof. Philip S. Yu.</p></div>
<div class="news-item"><time datetime="2025-07">Jul 2025</time><p><a href="#paper-mdgfm">Multi-Domain Graph Foundation Models</a> was accepted to <strong>ICML 2025</strong>.</p></div>
<div class="news-item"><time datetime="2024-09">Sep 2024</time><p><a href="#paper-infomgf">Beyond Redundancy (InfoMGF)</a> was accepted to <strong>NeurIPS 2024</strong>.</p></div>
</div>
</section>
<section id="publications" aria-labelledby="publications-title">
<div class="section-heading publications-heading"><h2 id="publications-title">Selected Publications<span class="count-badge">4</span></h2><a class="text-link" href="https://scholar.google.com/citations?user=W_fgHnwAAAAJ&hl=en" target="_blank" rel="noopener noreferrer" >Google Scholar<svg class="icon small" aria-hidden="true"><use href="#icon-arrow"/></svg></a></div>
<div class="publications-toolbar"><p><span class="self-example">Zhixiang Shen</span> is highlighted. <span class="equal-contribution">* Equal contribution.</span></p><div class="year-filters" role="group" aria-label="Filter publications by year" hidden><button type="button" class="selected" data-year="all" aria-pressed="true">All</button><button type="button" data-year="2025" aria-pressed="false">2025</button><button type="button" data-year="2024" aria-pressed="false">2024</button></div></div>
<p class="sr-only" id="filter-status" aria-live="polite"></p>
<div class="publication-list">
<article class="publication" id="paper-mdgfm" data-year="2025" aria-labelledby="title-mdgfm">
<div class="paper-visual">
<span class="venue-badge ">ICML 2025</span>
<a class="paper-figure" href="assets/papers/mdgfm.webp" target="_blank" rel="noopener" data-figure="MDGFM" data-caption="Figure 1 · Overall framework of MDGFM" data-paper="https://proceedings.mlr.press/v267/wang25dj.html" aria-label="Enlarge the MDGFM framework figure">
<img src="assets/papers/mdgfm.webp" width="1347" height="484" alt="MDGFM framework: multi-domain pre-training with topology alignment and target-domain adaptation" >
<span class="figure-expand"><svg class="icon small" aria-hidden="true"><use href="#icon-expand"/></svg></span>
</a>
<span class="paper-method">MDGFM<span>Graph foundation models</span></span>
</div>
<div class="paper-content">
<h3 id="title-mdgfm"><a class="" href="https://proceedings.mlr.press/v267/wang25dj.html" target="_blank" rel="noopener noreferrer" >Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment</a></h3>
<p class="authors">Shuo Wang<sup>*</sup>, Bokui Wang<sup>*</sup>, <strong class="self-author">Zhixiang Shen<sup>*</sup></strong>, Boyan Deng, Zhao Kang</p>
<p class="venue">International Conference on Machine Learning <strong>(ICML)</strong>, 2025</p>
<p class="publication-meta">PMLR 267 · pp. 64806–64821</p>
<p class="paper-takeaway">Aligning graph topologies for robust knowledge transfer across domains.</p>
<div class="paper-links"><a class="paper-button" href="https://proceedings.mlr.press/v267/wang25dj.html" target="_blank" rel="noopener noreferrer" >Paper<svg class="icon tiny" aria-hidden="true"><use href="#icon-arrow"/></svg></a><a class="paper-button" href="https://arxiv.org/abs/2502.02017" target="_blank" rel="noopener noreferrer" >arXiv</a><a class="paper-button" href="https://raw.githubusercontent.com/mlresearch/v267/main/assets/wang25dj/wang25dj.pdf" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-file"/></svg>PDF</a><a class="paper-button" href="https://github.com/wbkzwqtzw/MDGFM" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-code"/></svg>Code</a><a class="paper-button" href="assets/bibtex/mdgfm.bib" data-bib="mdgfm"><svg class="icon tiny" aria-hidden="true"><use href="#icon-quote"/></svg>BibTeX</a></div>
<details class="paper-summary"><summary>Research summary</summary><p>MDGFM combines feature alignment, adaptive weighting of attributes and topology, and graph refinement during self-supervised pre-training. A dual-prompt adaptation stage transfers the resulting representations to unseen domains with few labels. The work studies both transfer performance and resilience to unreliable graph connections.</p></details>
</div>
</article>
<article class="publication" id="paper-latgrl" data-year="2025" aria-labelledby="title-latgrl">
<div class="paper-visual">
<span class="venue-badge badge-teal">IEEE TNNLS 2025</span>
<a class="paper-figure" href="assets/papers/latgrl.webp" target="_blank" rel="noopener" data-figure="LatGRL" data-caption="Figure 3 · Overall framework of LatGRL" data-paper="https://doi.org/10.1109/TNNLS.2025.3540063" aria-label="Enlarge the LatGRL framework figure">
<img src="assets/papers/latgrl.webp" width="1890" height="900" alt="LatGRL framework: latent graph construction, dual-frequency filtering, and unsupervised representation learning" loading="lazy">
<span class="figure-expand"><svg class="icon small" aria-hidden="true"><use href="#icon-expand"/></svg></span>
</a>
<span class="paper-method">LatGRL<span>Heterogeneous graphs</span></span>
</div>
<div class="paper-content">
<h3 id="title-latgrl"><a class="" href="https://doi.org/10.1109/TNNLS.2025.3540063" target="_blank" rel="noopener noreferrer" >When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning</a></h3>
<p class="authors"><strong class="self-author">Zhixiang Shen</strong>, Zhao Kang</p>
<p class="venue">IEEE Transactions on Neural Networks and Learning Systems <strong>(TNNLS)</strong>, 2025</p>
<p class="publication-meta">Vol. 36, no. 6 · pp. 10283–10296 · DOI: 10.1109/TNNLS.2025.3540063</p>
<p class="paper-takeaway">Learning from both homophilic and heterophilic patterns in heterogeneous graphs.</p>
<div class="paper-links"><a class="paper-button" href="https://doi.org/10.1109/TNNLS.2025.3540063" target="_blank" rel="noopener noreferrer" >Paper<svg class="icon tiny" aria-hidden="true"><use href="#icon-arrow"/></svg></a><a class="paper-button" href="https://arxiv.org/abs/2409.00687" target="_blank" rel="noopener noreferrer" >arXiv</a><a class="paper-button" href="https://arxiv.org/pdf/2409.00687" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-file"/></svg>PDF</a><a class="paper-button" href="https://github.com/zxlearningdeep/LatGRL" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-code"/></svg>Code</a><a class="paper-button" href="assets/bibtex/latgrl.bib" data-bib="latgrl"><svg class="icon tiny" aria-hidden="true"><use href="#icon-quote"/></svg>BibTeX</a></div>
<details class="paper-summary"><summary>Research summary</summary><p>LatGRL examines semantic heterophily in heterogeneous graphs. It mines latent graphs using structural and attribute similarities, then combines low- and high-frequency information to adapt to individual nodes. The framework learns without labels and includes a scalable implementation for larger graphs.</p></details>
</div>
</article>
<article class="publication" id="paper-infomgf" data-year="2024" aria-labelledby="title-infomgf">
<div class="paper-visual">
<span class="venue-badge badge-purple">NeurIPS 2024</span>
<a class="paper-figure" href="assets/papers/infomgf.webp" target="_blank" rel="noopener" data-figure="InfoMGF" data-caption="Figure 2 · Overall framework of InfoMGF" data-paper="https://papers.nips.cc/paper_files/paper/2024/hash/380a0b16a7e6f8c5010f798c9f2d3c61-Abstract-Conference.html" aria-label="Enlarge the InfoMGF framework figure">
<img src="assets/papers/infomgf.webp" width="1546" height="686" alt="InfoMGF framework: graph refinement and fusion that retain shared and view-specific information" loading="lazy">
<span class="figure-expand"><svg class="icon small" aria-hidden="true"><use href="#icon-expand"/></svg></span>
</a>
<span class="paper-method">InfoMGF<span>Graph structure learning</span></span>
</div>
<div class="paper-content">
<h3 id="title-infomgf"><a class="" href="https://papers.nips.cc/paper_files/paper/2024/hash/380a0b16a7e6f8c5010f798c9f2d3c61-Abstract-Conference.html" target="_blank" rel="noopener noreferrer" >Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning</a></h3>
<p class="authors"><strong class="self-author">Zhixiang Shen<sup>*</sup></strong>, Shuo Wang<sup>*</sup>, Zhao Kang</p>
<p class="venue">Advances in Neural Information Processing Systems <strong>(NeurIPS)</strong>, 2024</p>
<p class="publication-meta">Volume 37 · pp. 31629–31658 · Main Conference Track</p>
<p class="paper-takeaway">Preserving shared and view-unique information while removing graph noise.</p>
<div class="paper-links"><a class="paper-button" href="https://papers.nips.cc/paper_files/paper/2024/hash/380a0b16a7e6f8c5010f798c9f2d3c61-Abstract-Conference.html" target="_blank" rel="noopener noreferrer" >Paper<svg class="icon tiny" aria-hidden="true"><use href="#icon-arrow"/></svg></a><a class="paper-button" href="https://arxiv.org/abs/2409.17386" target="_blank" rel="noopener noreferrer" >arXiv</a><a class="paper-button" href="https://papers.nips.cc/paper_files/paper/2024/file/380a0b16a7e6f8c5010f798c9f2d3c61-Paper-Conference.pdf" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-file"/></svg>PDF</a><a class="paper-button" href="https://github.com/zxlearningdeep/InfoMGF" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-code"/></svg>Code</a><a class="paper-button" href="assets/bibtex/infomgf.bib" data-bib="infomgf"><svg class="icon tiny" aria-hidden="true"><use href="#icon-quote"/></svg>BibTeX</a></div>
<details class="paper-summary"><summary>Research summary</summary><p>InfoMGF studies multiplex graphs whose different views contain useful information beyond their common signal. It learns refined graph structures and a fused graph, balancing shared and view-specific information while reducing irrelevant connections. The work evaluates the learned graph through downstream tasks and robustness experiments.</p></details>
</div>
</article>
<article class="publication" id="paper-bmgc" data-year="2024" aria-labelledby="title-bmgc">
<div class="paper-visual">
<span class="venue-badge badge-amber">ACM MM 2024</span>
<a class="paper-figure" href="assets/papers/bmgc.webp" target="_blank" rel="noopener" data-figure="BMGC" data-caption="Figure 2 · Overall framework of BMGC" data-paper="https://doi.org/10.1145/3664647.3681325" aria-label="Enlarge the BMGC framework figure">
<img src="assets/papers/bmgc.webp" width="1014" height="502" alt="BMGC framework: unsupervised dominant-view mining and dual-signal representation learning for clustering" loading="lazy">
<span class="figure-expand"><svg class="icon small" aria-hidden="true"><use href="#icon-expand"/></svg></span>
</a>
<span class="paper-method">BMGC<span>Multi-relational clustering</span></span>
</div>
<div class="paper-content">
<h3 id="title-bmgc"><a class="" href="https://doi.org/10.1145/3664647.3681325" target="_blank" rel="noopener noreferrer" >Balanced Multi-Relational Graph Clustering</a></h3>
<p class="authors"><strong class="self-author">Zhixiang Shen</strong>, Haolan He, Zhao Kang</p>
<p class="venue">ACM International Conference on Multimedia <strong>(ACM MM)</strong>, 2024</p>
<p class="publication-meta">32nd ACM Multimedia · pp. 4120–4128 · DOI: 10.1145/3664647.3681325</p>
<p class="paper-takeaway">Discovering the dominant view to address imbalance in multi-relational graphs.</p>
<div class="paper-links"><a class="paper-button" href="https://doi.org/10.1145/3664647.3681325" target="_blank" rel="noopener noreferrer" >Paper<svg class="icon tiny" aria-hidden="true"><use href="#icon-arrow"/></svg></a><a class="paper-button" href="https://arxiv.org/abs/2407.16863" target="_blank" rel="noopener noreferrer" >arXiv</a><a class="paper-button" href="https://arxiv.org/pdf/2407.16863" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-file"/></svg>PDF</a><a class="paper-button" href="https://github.com/zxlearningdeep/BMGC" target="_blank" rel="noopener noreferrer" ><svg class="icon tiny" aria-hidden="true"><use href="#icon-code"/></svg>Code</a><a class="paper-button" href="assets/bibtex/bmgc.bib" data-bib="bmgc"><svg class="icon tiny" aria-hidden="true"><use href="#icon-quote"/></svg>BibTeX</a></div>
<details class="paper-summary"><summary>Research summary</summary><p>BMGC addresses unequal view quality in multi-relational graph clustering. An unsupervised procedure identifies a dominant view during training, and two complementary signals guide representation learning. A graph-quality metric connects structural differences to clustering behavior, with analysis on real and synthetic graphs.</p></details>
</div>
</article>
</div>
</section>
<section id="education" aria-labelledby="education-title">
<div class="section-heading"><h2 id="education-title">Education</h2></div>
<div class="education-panel">
<div class="education-item"><a class="institution-logo uic-logo" href="https://www.uic.edu/" target="_blank" rel="noopener noreferrer"><img src="assets/institutions/uic.png" width="600" height="365" alt="University of Illinois Chicago official logo" loading="lazy"></a><div><h3><a class="" href="https://www.uic.edu/" target="_blank" rel="noopener noreferrer" >University of Illinois Chicago</a></h3><p>Ph.D. in Computer Science</p><p class="education-detail">Advisor: Prof. Philip S. Yu</p></div><time datetime="2026-08">Aug 2026 – Present</time></div>
<div class="education-item"><a class="institution-logo uestc-logo" href="https://en.uestc.edu.cn/" target="_blank" rel="noopener noreferrer"><img src="assets/institutions/uestc.jpg" width="225" height="212" alt="University of Electronic Science and Technology of China official emblem" loading="lazy"></a><div><h3><a class="" href="https://en.uestc.edu.cn/" target="_blank" rel="noopener noreferrer" >University of Electronic Science and Technology of China</a></h3><p>B.Eng. in Computer Science</p><p class="education-detail">Research with Prof. Zhao Kang</p></div><span class="education-date">2021 – 2025</span></div>
</div>
</section>
<section id="services" aria-labelledby="services-title">
<div class="section-heading"><h2 id="services-title">Academic Service</h2></div>
<div class="service-grid">
<div class="service-card"><h3>Conference Reviewer</h3><ul class="conference-list"><li><span>NeurIPS</span><span>2025, 2026</span></li><li><span>ICLR</span><span>2026, 2027</span></li><li><span>IJCAI</span><span>2025</span></li><li><span>ACM Multimedia</span><span>2025</span></li></ul></div>
<div class="service-card"><h3>Journal Reviewer</h3><ul class="journal-list"><li>IEEE Transactions on Pattern Analysis and Machine Intelligence <strong>(TPAMI)</strong></li><li>IEEE Transactions on Neural Networks and Learning Systems <strong>(TNNLS)</strong></li><li>IEEE Transactions on Circuits and Systems for Video Technology <strong>(TCSVT)</strong></li></ul></div>
</div>
</section>
<footer><p>© 2026 Zhixiang Shen<span>Last updated: September 2026</span></p><a href="#about">Back to top ↑</a></footer>
</main>
</div>
<dialog id="figure-dialog" class="figure-dialog" aria-labelledby="figure-dialog-title">
<div class="dialog-header"><h2 id="figure-dialog-title">Research figure</h2><button type="button" class="icon-button" data-close aria-label="Close figure"><svg class="icon " aria-hidden="true"><use href="#icon-close"/></svg></button></div>
<div class="figure-dialog-body"><img id="dialog-figure" alt=""></div>
<div class="figure-dialog-footer"><p id="dialog-caption"></p><a id="dialog-paper" target="_blank" rel="noopener noreferrer">Open paper ↗</a></div>
</dialog>
<dialog id="bib-dialog" class="bib-dialog" aria-labelledby="bib-dialog-title">
<div class="dialog-header"><h2 id="bib-dialog-title">BibTeX citation</h2><button type="button" class="icon-button" data-close aria-label="Close citation"><svg class="icon " aria-hidden="true"><use href="#icon-close"/></svg></button></div>
<pre id="bib-content" tabindex="0"></pre>
<div class="bib-dialog-footer"><a id="bib-download" download>Download .bib</a><button type="button" id="copy-bib" class="copy-button"><svg class="icon small" aria-hidden="true"><use href="#icon-copy"/></svg><span>Copy citation</span></button></div>
<p class="sr-only" id="copy-status" aria-live="polite"></p>
</dialog>
<template id="bib-mdgfm">@inproceedings{wang2025multidomain,
title = {Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment},
author = {Wang, Shuo and Wang, Bokui and Shen, Zhixiang and Deng, Boyan and Kang, Zhao},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
series = {Proceedings of Machine Learning Research},
volume = {267},
pages = {64806--64821},
year = {2025},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v267/wang25dj.html}
}</template><template id="bib-latgrl">@article{shen2025heterophily,
title = {When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning},
author = {Shen, Zhixiang and Kang, Zhao},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
volume = {36},
number = {6},
pages = {10283--10296},
year = {2025},
doi = {10.1109/TNNLS.2025.3540063}
}</template><template id="bib-infomgf">@inproceedings{NEURIPS2024_380a0b16,
author = {Shen, Zhixiang and Wang, Shuo and Kang, Zhao},
booktitle = {Advances in Neural Information Processing Systems},
doi = {10.52202/079017-0993},
editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
pages = {31629--31658},
publisher = {Curran Associates, Inc.},
title = {Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/380a0b16a7e6f8c5010f798c9f2d3c61-Paper-Conference.pdf},
volume = {37},
year = {2024}
}</template><template id="bib-bmgc">@inproceedings{shen2024balanced,
title = {Balanced Multi-Relational Graph Clustering},
author = {Shen, Zhixiang and He, Haolan and Kang, Zhao},
booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
pages = {4120--4128},
year = {2024},
doi = {10.1145/3664647.3681325},
publisher = {Association for Computing Machinery}
}</template>
</body>
</html>