Estimating Correlation Clustering Cost in Node-Arrival Stream

TL;DR

This paper introduces a streaming algorithm for correlation clustering in node-arrival models, achieving near-optimal performance with minimal memory and multiple passes, suitable for real-world data streams.

Contribution

The paper proposes C$^4$Approx, a novel streaming algorithm for correlation clustering in node-arrival streams, with theoretical guarantees and practical efficiency.

Findings

Achieves sublinear space approximation of correlation clustering cost.

Performs comparably to classic algorithms using only 2% node storage.

Works effectively on real-world datasets with sparse graphs.

Abstract

We study the correlation clustering problem in the node-arrival data stream model. Unlike previous work, where the stream consists of the graph's edges, we focus on the setting in which the stream contains only the nodes. This model better reflects many real-world scenarios in which the data stream naturally consists of raw objects (e.g., images, tweets), and the similar/dissimilar edges are derived through a similarity function. We present C$^4$Approx, a streaming algorithm that approximates the cost of correlation clustering using sublinear space in the number of nodes and a constant number of passes. We further complement this result with lower bounds. Experiments on real-world datasets show that by storing only 2% of the nodes, our algorithm achieves performance comparable to the classic Pivot algorithm and the more recent PrunedPivot algorithm, even on sparse graphs.