add static export

Author: Boltzmachine

app/publications/page.tsx

@@ -6,6 +6,14 @@ import { useSearchParams } from "next/navigation";
 import { publications } from "@/config/publications";
 import { PublicationTags } from "@/components/tag";
 
+function Links({ paper, code, page }: { paper: string, code: string | null; page: string | null }) {
+  if (code === null) {
+    return <Link href={paper}>paper</Link>;
+  } else {
+    return <> <Link href={paper}>paper</Link> / <Link href={code}>code</Link> </>
+  }
+}
+
 export default function PublicationsPage() {
   const searchParams = useSearchParams();
 
@@ -34,8 +42,7 @@ export default function PublicationsPage() {
               </div>
               <div className="flex flex-col mx-4 my-2">
                 <p className="pb-1 text-sm font-bold">
-                  {publication.venue}, <Link href={publication.code}>code</Link>{" "}
-                  / <Link href={publication.paper}>paper</Link>
+                  {publication.venue}, <Links paper={publication.paper} code={publication.code} page={publication.page} />
                 </p>
                 <p className="pb-1 text-sm">{publication.authors}</p>
                 <p className="pb-1">{publication.abstract}</p>

components/tag.tsx

@@ -40,7 +40,7 @@ function PublicationTag({ tag }: { tag: Tag }) {
 
   return (
     <>
-      <Chip color={ color }>{name}</Chip>
+      <Chip color={ color as any }>{name}</Chip>
     </>
   );
 }

config/publications.ts

@@ -20,6 +20,7 @@ export const publications = [
     title: "Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer",
     authors: "Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin",
     venue: "MLSB Workshop, NeurIPS, 2023",
+    page: null,
     code: "https://github.com/Graph-and-Geometric-Learning/Frame-Averaging-Transformer",
     paper: "https://arxiv.org/abs/2406.09586",
     abstract: "Propose a novel unsupervised aptamer screening paradigm and FAFormer, a frame averaging-based equivariant transformer architecture.",
@@ -30,6 +31,7 @@ export const publications = [
     title: "From Similarity to Superiority: Channel Clustering for Time Series Forecasting",
     authors: "Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, Rex Ying",
     venue: "Arxiv Preprint",
+    page: null,
     code: null,
     paper: "https://arxiv.org/pdf/2404.01340",
     abstract: "We developed a novel and adaptable Channel Clustering Module (CCM), which dynamically groups channels characterized by intrinsic similarities and leverages cluster identity, instead of channel identity, to improve time series forecasting performance.",
@@ -54,6 +56,7 @@ export const publications = [
     title: "HEART: Learning Better Representation of EHR Data with a Heterogeneous Relation-Aware Transformer",
     authors: "T Huang, SA Rizvi, R Krishna Thakur, V Socrates, M Gupta, D Dijk, RA Taylor, R Ying",
     venue: "Preprint",
+    page: null,
     code: null,
     paper: "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4904741",
     abstract: "We propose HEART, a pretrained language model for structured EHR data. HEART seamlessly encodes heterogeneous medical entity information through a novel relation embedding module and a multi-level attention scheme.",
@@ -64,6 +67,7 @@ export const publications = [
     title: "Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability",
     authors: "Jiasheng Zhang, Rex Ying, Jie Shao",
     venue: "SIGMOD 2025",
+    page: null,
     code: "https://github.com/zjs123/ANoT",
     paper: "https://arxiv.org/abs/2408.00872",
     abstract: "we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs.",
@@ -103,6 +107,7 @@ export const publications = [
     title: "Explaining Graph Neural Networks via Structure-aware Interaction Index",
     authors: "Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying",
     venue: "ICML 2024",
+    page: null,
     code: "https://github.com/ngocbh/MAGE",
     paper: "https://arxiv.org/abs/2405.14352",
     abstract: "We introduces a novel interaction index, namely the Myerson-Taylor interaction index, that internalizes the graph structure into attributing the node values of Shapley value and the interaction values among nodes. We prove that that the Myerson-Taylor index is the unique one that satisfies a system of five natural axioms accounting for graph structure and high-order interaction among nodes. We propose MAGE, a new graph explainer that uses the second-order Myerson-Taylor index to identify the most important motifs influencing the model prediction.",
@@ -113,6 +118,7 @@ export const publications = [
     title: "TempMe: Towards the explainability of temporal graph neural networks via motif discovery",
     authors: "Jialin Chen, Rex Ying",
     venue: "NeurIPS 2023",
+    page: null,
     code: "https://github.com/Graph-and-Geometric-Learning/TempME",
     paper: "https://proceedings.neurips.cc/paper_files/paper/2023/hash/5c5bc3553815adb4d1a8a5b8701e41a9-Abstract-Conference.html",
     tags: [Tag.TrustworthyAI],
@@ -138,6 +144,7 @@ export const publications = [
     title: "Learning to Group Auxiliary Datasets for Molecule",
     authors: "Tinglin Huang, Ziniu Hu, Rex Ying",
     venue: "NeurIPS 2023",
+    page: null,
     code: "https://github.com/Graph-and-Geometric-Learning/MolGroup",
     paper: "https://arxiv.org/abs/2307.04052",
     abstract: "Propose a routing-based auxiliary dataset grouping method to enhance model performance on molecule datasets with limited labels.",
@@ -148,6 +155,7 @@ export const publications = [
     title: "BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs",
     authors: "Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang",
     venue: "SIGKDD 2023",
+    page: null,
     code: "https://github.com/THUDM/BatchSampler",
     paper: "https://arxiv.org/abs/2306.03355v1",
     abstract: "Propose a global negative sampling method for contrastive learning by modeling negative sampling as graph sampling on a proximity graph.",

next.config.js

@@ -1,5 +1,8 @@
 /** @type {import('next').NextConfig} */
 const nextConfig = {
+    basePath: "/lab-website",
+    output: "export",
+    reactStrictMode: true,
     images: {
         formats: ["image/avif", "image/webp"],
         remotePatterns: [