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@@ -265,6 +265,146 @@ a {
font-weight: 600;
}
.graph-project {
--graph-paper: #f4f1e8;
--graph-panel: #fffdf6;
--graph-ink: #161512;
--graph-soft: #39362d;
--graph-muted: #6d675a;
--graph-line: rgba(22, 21, 18, 0.16);
--graph-accent: #0f6f77;
--graph-accent-strong: #0c4f58;
--graph-warm: #e28f45;
--graph-code: #171813;
--graph-code-text: #f2ead9;
background:
linear-gradient(90deg, rgba(22, 21, 18, 0.04) 1px, transparent 1px),
linear-gradient(180deg, rgba(22, 21, 18, 0.035) 1px, transparent 1px),
var(--graph-paper);
background-size: 4.5rem 4.5rem;
color: var(--graph-ink);
}
:root[data-theme="dark"] .graph-project {
--graph-paper: #111315;
--graph-panel: #181c1d;
--graph-ink: #f5f0e6;
--graph-soft: #ddd5c5;
--graph-muted: #a9a091;
--graph-line: rgba(245, 240, 230, 0.15);
--graph-accent: #78c7cf;
--graph-accent-strong: #93dbe2;
--graph-warm: #e3a15f;
--graph-code: #0d0f10;
--graph-code-text: #f5f0e6;
}
.graph-project .text-graph-ink {
color: var(--graph-ink);
}
.graph-project .text-graph-soft {
color: var(--graph-soft);
}
.graph-project .text-graph-muted {
color: var(--graph-muted);
}
.graph-project .text-graph-accent {
color: var(--graph-accent);
}
.graph-project .text-graph-code-text {
color: var(--graph-code-text);
}
.graph-project .bg-graph-paper {
background-color: var(--graph-paper);
}
.graph-project .bg-graph-panel {
background-color: var(--graph-panel);
}
.graph-project .bg-graph-line {
background-color: var(--graph-line);
}
.graph-project .bg-graph-code {
background-color: var(--graph-code);
}
.graph-project .border-graph-line {
border-color: var(--graph-line);
}
.graph-project .bg-graph-accent {
background-color: var(--graph-accent);
}
.graph-project .hover\:text-graph-accent:hover {
color: var(--graph-accent);
}
.graph-project .hover\:border-graph-accent:hover {
border-color: var(--graph-accent);
}
.graph-hero {
background:
radial-gradient(42rem 24rem at 16% 18%, rgba(226, 143, 69, 0.18), transparent 70%),
radial-gradient(38rem 24rem at 82% 10%, rgba(15, 111, 119, 0.16), transparent 72%);
}
.graph-hero-card {
overflow: hidden;
border: 1px solid var(--graph-line);
border-radius: 0.5rem;
background: var(--graph-panel);
box-shadow: 0 34px 80px rgba(22, 21, 18, 0.12);
}
.graph-card-top {
display: flex;
justify-content: space-between;
gap: 1rem;
border-bottom: 1px solid var(--graph-line);
padding: 0.85rem 1rem;
color: var(--graph-muted);
font-family: var(--font-mono), monospace;
font-size: 0.68rem;
text-transform: uppercase;
}
.graph-matrix {
display: grid;
grid-template-columns: repeat(6, minmax(0, 1fr));
width: min(17rem, 58vw);
overflow: hidden;
border: 1px solid var(--graph-line);
background: #202f33;
}
.graph-matrix span {
aspect-ratio: 1;
border-right: 1px solid rgba(244, 241, 232, 0.14);
border-bottom: 1px solid rgba(244, 241, 232, 0.14);
background: #275b63;
}
.graph-matrix span.is-hot {
background: #f0d83a;
}
.graph-kicker {
color: var(--graph-muted);
font-family: var(--font-mono), monospace;
font-size: 0.72rem;
letter-spacing: 0;
text-transform: uppercase;
}
@media (min-width: 1280px) {
.side-note {
float: right;

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@@ -30,6 +30,9 @@ export default function Home() {
<Link href={latestPostHref} className="transition-colors hover:text-accent">
Latest essay
</Link>
<Link href="/projects" className="transition-colors hover:text-accent">
Projects
</Link>
<Link href="/blog" className="transition-colors hover:text-accent">
Writing
</Link>

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@@ -0,0 +1,307 @@
import Image from "next/image";
import Link from "next/link";
import type { Metadata } from "next";
import architectureImage from "@/public/images/graph-attention-topology/model-architecture.png";
import simulationImage from "@/public/images/graph-attention-topology/simulation-dynamics.png";
import resultsImage from "@/public/images/graph-attention-topology/f1-results.png";
import attentionImage from "@/public/images/graph-attention-topology/attention-training.png";
const paperLinks = [
{ href: "https://arxiv.org/abs/2408.15449", label: "arXiv" },
{ href: "https://arxiv.org/pdf/2408.15449", label: "PDF" },
{ href: "https://doi.org/10.48550/arXiv.2408.15449", label: "DOI" },
];
const highlights = [
{
value: "No known graph",
label: "Learns topology without prior adjacency examples.",
},
{
value: "Unknown dynamics",
label: "Uses observed states instead of hand-specified equations.",
},
{
value: "Attention as edges",
label: "Interprets learned attention scores as the graph estimate.",
},
];
const steps = [
"Embed every agent into a shared latent space.",
"Project embeddings into key/query vectors and compute pairwise attention.",
"Translate observed agent states into values.",
"Predict the next state and read the attention matrix as topology.",
];
export const metadata: Metadata = {
title: "Graph Attention Topology Inference",
description:
"Project page for Graph Attention Inference of Network Topology in Multi-Agent Systems.",
openGraph: {
title: "Graph Attention Inference of Network Topology in Multi-Agent Systems",
description:
"A graph-attention approach for inferring hidden network topology from multi-agent trajectories.",
type: "article",
publishedTime: "2024-10-26",
images: [
{
url: "/images/graph-attention-topology/model-architecture.png",
width: architectureImage.width,
height: architectureImage.height,
alt: "Graph attention model architecture",
},
],
},
};
function ExternalLink({ href, label }: { href: string; label: string }) {
return (
<a
href={href}
target="_blank"
rel="noopener noreferrer"
className="rounded-full border border-graph-line bg-graph-panel px-4 py-2 text-sm font-medium text-graph-ink transition-colors hover:border-graph-accent hover:text-graph-accent"
>
{label}
</a>
);
}
function Figure({
src,
alt,
caption,
className = "",
}: {
src: typeof architectureImage;
alt: string;
caption: string;
className?: string;
}) {
return (
<figure className={`overflow-hidden border border-graph-line bg-graph-panel ${className}`}>
<div className="bg-white">
<Image
src={src}
alt={alt}
sizes="(max-width: 768px) 100vw, 58rem"
className="h-auto w-full"
/>
</div>
<figcaption className="border-t border-graph-line px-4 py-3 font-mono text-[0.72rem] leading-5 text-graph-muted">
{caption}
</figcaption>
</figure>
);
}
export default function GraphAttentionProjectPage() {
return (
<div className="graph-project min-h-screen bg-graph-paper text-graph-ink">
<section className="graph-hero px-5 pb-14 pt-24 sm:px-6 sm:pb-20 sm:pt-28">
<div className="mx-auto grid max-w-[78rem] gap-10 lg:grid-cols-[minmax(0,1fr)_minmax(23rem,32rem)] lg:items-end">
<div className="space-y-7">
<div className="flex flex-wrap items-center gap-3 font-mono text-[0.72rem] uppercase text-graph-muted">
<Link href="/projects" className="transition-colors hover:text-graph-accent">
Projects
</Link>
<span className="h-px w-8 bg-graph-line" />
<span>MECC 2024</span>
<span className="h-px w-8 bg-graph-line" />
<span>Multi-Agent Systems</span>
</div>
<div className="space-y-5">
<h1 className="max-w-[54rem] text-balance font-sans text-[clamp(3.1rem,8vw,7.6rem)] font-semibold leading-[0.86] tracking-normal text-graph-ink">
Graph Attention Inference of Network Topology
</h1>
<p className="max-w-[43rem] text-[1.12rem] leading-8 text-graph-soft sm:text-[1.25rem]">
Inferring the hidden graph behind a multi-agent system by training attention to predict what each agent does next.
</p>
</div>
<div className="flex flex-wrap gap-2 text-[0.95rem] text-graph-soft">
<span>Akshay Kolli</span>
<span>/</span>
<span>Reza Azadeh</span>
<span>/</span>
<span>Kshitij Jerath</span>
</div>
<div className="flex flex-wrap gap-3">
{paperLinks.map((link) => (
<ExternalLink key={link.href} {...link} />
))}
</div>
</div>
<div className="graph-hero-card">
<div className="graph-card-top">
<span>attention matrix</span>
<span>unknown graph</span>
</div>
<div className="grid grid-cols-[1fr_auto] gap-4 p-4">
<div className="graph-matrix" aria-hidden>
{Array.from({ length: 36 }).map((_, index) => (
<span
key={index}
className={(index % 7 === 0 || [2, 11, 15, 22, 29, 34].includes(index)) ? "is-hot" : ""}
/>
))}
</div>
<div className="flex flex-col justify-between py-1 font-mono text-[0.68rem] uppercase text-graph-muted">
<span>query</span>
<span>key</span>
<span>edge</span>
</div>
</div>
<div className="border-t border-graph-line bg-white p-3">
<Image
src={architectureImage}
alt="Graph attention model architecture"
priority
sizes="(max-width: 1024px) 92vw, 32rem"
className="mx-auto h-auto max-h-[30rem] w-auto"
/>
</div>
</div>
</div>
</section>
<section className="border-y border-graph-line bg-graph-panel px-5 py-5 sm:px-6">
<div className="mx-auto grid max-w-[78rem] gap-4 md:grid-cols-3">
{highlights.map((item) => (
<div key={item.value} className="space-y-2 border-l border-graph-line pl-4">
<p className="text-[1.05rem] font-semibold text-graph-ink">{item.value}</p>
<p className="text-[0.92rem] leading-6 text-graph-muted">{item.label}</p>
</div>
))}
</div>
</section>
<div className="mx-auto max-w-[78rem] px-5 py-14 sm:px-6 sm:py-20">
<div className="grid gap-12 lg:grid-cols-[14rem_minmax(0,1fr)] lg:gap-16">
<aside className="hidden lg:block">
<nav className="sticky top-24 space-y-3 font-mono text-[0.72rem] uppercase text-graph-muted">
<a href="#overview" className="block transition-colors hover:text-graph-accent">Overview</a>
<a href="#method" className="block transition-colors hover:text-graph-accent">Method</a>
<a href="#results" className="block transition-colors hover:text-graph-accent">Results</a>
<a href="#citation" className="block transition-colors hover:text-graph-accent">Citation</a>
</nav>
</aside>
<div className="space-y-20">
<section id="overview" className="grid gap-8 lg:grid-cols-[minmax(0,0.9fr)_minmax(18rem,0.7fr)]">
<div className="space-y-5">
<p className="graph-kicker">Overview</p>
<h2 className="max-w-[42rem] text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
Predict the next state, then read the graph from what the model attended to.
</h2>
<div className="space-y-5 text-[1.02rem] leading-8 text-graph-soft">
<p>
The paper studies a practical problem in networked multi-agent systems: the agents move, synchronize, or converge, but the interaction graph behind that behavior is not given.
</p>
<p>
The model is trained on state trajectories from consensus dynamics and Kuramoto oscillators. During prediction, attention scores between agent embeddings become an interpretable approximation of the adjacency matrix.
</p>
</div>
</div>
<Figure
src={simulationImage}
alt="Consensus and Kuramoto simulation examples with adjacency matrices"
caption="Simulation examples: consensus dynamics and Kuramoto oscillators paired with their hidden adjacency matrices."
className="rounded-md"
/>
</section>
<section id="method" className="space-y-8">
<div className="grid gap-8 lg:grid-cols-[minmax(0,0.8fr)_minmax(0,1fr)] lg:items-start">
<div className="space-y-5">
<p className="graph-kicker">Method</p>
<h2 className="text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
A topology estimate falls out of the attention layer.
</h2>
<p className="text-[1.02rem] leading-8 text-graph-soft">
Instead of supervising the graph directly, the model learns to forecast the system. The attention matrix is then thresholded and compared with the true graph.
</p>
</div>
<ol className="grid gap-3">
{steps.map((step, index) => (
<li key={step} className="flex gap-4 border-t border-graph-line py-4">
<span className="font-mono text-sm text-graph-accent">
{(index + 1).toString().padStart(2, "0")}
</span>
<span className="text-[1rem] leading-7 text-graph-soft">{step}</span>
</li>
))}
</ol>
</div>
<Figure
src={architectureImage}
alt="Architecture diagram showing agent embeddings, key-query attention, values, predictions, and loss"
caption="Architecture: embeddings produce key/query vectors; observed states produce values; the attention matrix approximates adjacency."
className="rounded-md"
/>
</section>
<section id="results" className="space-y-8">
<div className="max-w-[46rem] space-y-5">
<p className="graph-kicker">Results</p>
<h2 className="text-balance text-4xl font-semibold leading-tight text-graph-ink sm:text-5xl">
Strongest graph recovery appears with smaller systems, and more simulations help larger systems.
</h2>
<p className="text-[1.02rem] leading-8 text-graph-soft">
F1 link-prediction scores are above a random baseline for both tested dynamics. Consensus dynamics are easier than Kuramoto oscillators, while additional simulation data improves larger-agent inference.
</p>
</div>
<Figure
src={resultsImage}
alt="F1 score results for consensus dynamics and Kuramoto oscillators"
caption="F1 results across system size and number of simulations for consensus dynamics and Kuramoto oscillators."
className="rounded-md"
/>
<div className="grid gap-6 md:grid-cols-[minmax(0,0.78fr)_minmax(0,1fr)] md:items-start">
<div className="rounded-md border border-graph-line bg-graph-panel p-5">
<p className="font-mono text-[0.72rem] uppercase text-graph-muted">training behavior</p>
<p className="mt-3 text-[1.02rem] leading-8 text-graph-soft">
Attention first learns the obvious self-dependencies, then gradually recovers inter-agent structure as training progresses.
</p>
</div>
<Figure
src={attentionImage}
alt="Attention matrices over training epochs and predicted graphs"
caption="Attention values through training stages, from true graph to predicted graph."
className="rounded-md"
/>
</div>
</section>
<section id="citation" className="space-y-5 border-t border-graph-line pt-10">
<p className="graph-kicker">Citation</p>
<h2 className="text-3xl font-semibold leading-tight text-graph-ink">Paper</h2>
<p className="max-w-[46rem] text-[1.02rem] leading-8 text-graph-soft">
Graph Attention Inference of Network Topology in Multi-Agent Systems. Akshay Kolli, Reza Azadeh, and Kshitij Jerath. Accepted at the Modeling and Estimation Control Conference, 2024.
</p>
<pre className="overflow-x-auto rounded-md border border-graph-line bg-graph-code p-5 text-sm leading-7 text-graph-code-text">
{`@misc{kolli2024graphattentioninference,
title={Graph Attention Inference of Network Topology in Multi-Agent Systems},
author={Kolli, Akshay and Azadeh, Reza and Jerath, Kshitij},
year={2024},
eprint={2408.15449},
archivePrefix={arXiv},
primaryClass={cs.MA}
}`}
</pre>
</section>
</div>
</div>
</div>
</div>
);
}

109
app/projects/page.tsx Normal file
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@@ -0,0 +1,109 @@
import Image from "next/image";
import Link from "next/link";
import type { Metadata } from "next";
import graphAttentionImage from "@/public/images/graph-attention-topology/model-architecture.png";
const projects = [
{
href: "/projects/graph-attention-topology",
title: "Graph Attention Inference of Network Topology",
eyebrow: "MECC 2024 / Multi-Agent Systems",
description:
"Inferring hidden interaction graphs from multi-agent trajectories by training attention to predict each agent's next state.",
status: "Paper project",
year: "2024",
image: graphAttentionImage,
imageAlt: "Graph attention model architecture diagram",
tags: ["Graph attention", "Topology inference", "Kuramoto oscillators"],
},
];
export const metadata: Metadata = {
title: "Projects",
description: "Research projects and paper pages by Akshay Kolli.",
openGraph: {
title: "Projects",
description: "Research projects and paper pages by Akshay Kolli.",
},
};
export default function ProjectsPage() {
return (
<div className="page-frame py-20 sm:py-24">
<div className="mx-auto max-w-[72rem] space-y-10">
<header className="grid gap-6 border-b border-line pb-10 lg:grid-cols-[minmax(0,1fr)_16rem] lg:items-end">
<div className="space-y-4">
<p className="eyebrow">Projects</p>
<h1 className="max-w-[44rem] text-balance font-sans text-5xl font-medium leading-[0.96] text-ink sm:text-6xl lg:text-7xl">
Research pages and technical artifacts.
</h1>
<p className="max-w-[36rem] text-[1rem] leading-8 text-muted">
A compact index of paper pages, experiments, and systems work.
</p>
</div>
<p className="font-mono text-[0.72rem] uppercase leading-6 text-muted-strong lg:text-right">
{projects.length} active {projects.length === 1 ? "project" : "projects"}
</p>
</header>
<div className="grid gap-6">
{projects.map((project) => (
<article
key={project.href}
className="group grid overflow-hidden rounded-md border border-line bg-paper-strong transition-colors hover:border-line-strong md:grid-cols-[minmax(0,1fr)_18rem]"
>
<div className="flex min-h-[21rem] flex-col justify-between gap-8 p-6 sm:p-8">
<div className="space-y-5">
<div className="flex flex-wrap items-center gap-x-4 gap-y-2 font-mono text-[0.72rem] uppercase text-muted-strong">
<span>{project.eyebrow}</span>
<span>{project.year}</span>
<span>{project.status}</span>
</div>
<div className="space-y-3">
<Link href={project.href} className="block">
<h2 className="max-w-[42rem] text-balance font-sans text-[2rem] font-medium leading-tight text-ink transition-colors group-hover:text-accent sm:text-[2.6rem]">
{project.title}
</h2>
</Link>
<p className="max-w-[38rem] text-[1rem] leading-8 text-muted">
{project.description}
</p>
</div>
</div>
<div className="flex flex-wrap items-center gap-x-4 gap-y-3">
<Link
href={project.href}
className="font-medium text-ink underline decoration-line-strong underline-offset-4 transition-colors hover:text-accent hover:decoration-accent"
>
Open project
</Link>
<div className="flex flex-wrap gap-3 font-mono text-[0.68rem] uppercase text-muted-strong">
{project.tags.map((tag) => (
<span key={tag}>{tag}</span>
))}
</div>
</div>
</div>
<Link
href={project.href}
aria-label={`Open ${project.title}`}
className="relative min-h-[18rem] border-t border-line bg-white md:border-l md:border-t-0"
>
<Image
src={project.image}
alt={project.imageAlt}
fill
sizes="(max-width: 768px) 100vw, 18rem"
className="object-contain p-5 transition-transform duration-300 group-hover:scale-[1.025]"
/>
</Link>
</article>
))}
</div>
</div>
</div>
);
}

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@@ -12,6 +12,8 @@ export default function sitemap(): MetadataRoute.Sitemap {
return [
{ url: 'https://akkolli.net', lastModified: latestPostDate },
{ url: 'https://akkolli.net/projects', lastModified: new Date('2024-10-26') },
{ url: 'https://akkolli.net/projects/graph-attention-topology', lastModified: new Date('2024-10-26') },
{ url: 'https://akkolli.net/blog', lastModified: latestPostDate },
{ url: 'https://akkolli.net/resume' },
...blogEntries,