Space-Efficient Random Walks on Streaming Graphs
Serafeim Papadias, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, Volker Markl

TL;DR
Wharf is a system designed to efficiently maintain and update random walks on streaming graphs, using compression and pruning techniques to handle high rates of graph changes with high throughput and low latency.
Contribution
It introduces a novel, compressed data structure combining binary trees and pairing functions for real-time random walk updates on streaming graphs.
Findings
Outperforms baseline methods in throughput and latency
Maintains high accuracy with compressed data structures
Handles high-frequency graph updates efficiently
Abstract
Graphs in many applications, such as social networks and IoT, are inherently streaming, involving continuous additions and deletions of vertices and edges at high rates. Constructing random walks in a graph, i.e., sequences of vertices selected with a specific probability distribution, is a prominent task in many of these graph applications as well as machine learning (ML) on graph-structured data. In a streaming scenario, random walks need to constantly keep up with the graph updates to avoid stale walks and thus, performance degradation in the downstream tasks. We present Wharf, a system that efficiently stores and updates random walks on streaming graphs. It avoids a potential size explosion by maintaining a compressed, high-throughput, and low-latency data structure. It achieves (i) the succinct representation by coupling compressed purely functional binary trees and pairing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCaching and Content Delivery · Peer-to-Peer Network Technologies · Opportunistic and Delay-Tolerant Networks
