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   <span class="na">publisher</span> <span class="p">=</span> <span class="s">{American Association of Physics Teachers}</span>
 <span class="p">}</span></code></pre></figure> </div> </div> </div> </li></ol>  -->
 
-<ol class="bibliography"><li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge"><a href="" rel="external nofollow noopener" target="_blank">WebConf24</a></abbr></div> <div id="PhysRev.47.777" class="col-sm-8"> <div class="title"> Graph Principal Flow Network for Conditional Graph Generation</div> <div class="author"> Zhanfeng Mo*, <em>Tianze Luo*</em>, and Sinno Jialin Pan </div> <div class="periodical"> <em>The Web Conference (WWW) </em>, 2024</div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="badges">  </div> <div class="abstract hidden"> <p>Conditional graph generation is crucial and challenging since the conditional distribution of graph topology and feature is complicated and the semantic feature is hard to capture by the generative model. In this work, we propose a novel graph conditional generative model, termed Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate graphs from low- to high-frequency components. Our GPrinFlowNet effectively captures the subtle yet essential semantic features of graph topology, resulting in high-quality generated graph data given a required condition. Extensive experiments and ablation studies showcase that our model achieves state-of-the-art performance compared to existing conditional graph generation models.</p> </div> </div> </div> </li></ol>
+<ol class="bibliography"><li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge"><a href="" rel="external nofollow noopener" target="_blank">WebConf</a></abbr></div> <div id="PhysRev.47.777" class="col-sm-8"> <div class="title"> Graph Principal Flow Network for Conditional Graph Generation</div> <div class="author"> Zhanfeng Mo*, <em>Tianze Luo*</em>, and Sinno Jialin Pan </div> <div class="periodical"> <em>The Web Conference (WWW) </em>, 2024</div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="badges">  </div> <div class="abstract hidden"> <p>Conditional graph generation is crucial and challenging since the conditional distribution of graph topology and feature is complicated and the semantic feature is hard to capture by the generative model. In this work, we propose a novel graph conditional generative model, termed Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate graphs from low- to high-frequency components. Our GPrinFlowNet effectively captures the subtle yet essential semantic features of graph topology, resulting in high-quality generated graph data given a required condition. Extensive experiments and ablation studies showcase that our model achieves state-of-the-art performance compared to existing conditional graph generation models.</p> </div> </div> </div> </li></ol>