PEAR

Brought to you by: jo2lxq

Last Update: 2015-08-07

Get an email when there's a new version of PEAR

PEAR provides a lightweight message delivery framework over intermittenttly connected networks. This is an implementation of a paper "Mobility entropy and message routing in community-structured delay tolerant networks" (ACM AINTEC2008).

Project Activity

See All Activity >

Follow PEAR

PEAR Web Site

Other Useful Business Software

Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure

Native application identity and user-based security for your Azure cloud

Gain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.

Get a free trial

Rate This Project

User Reviews

Be the first to post a review of PEAR!

Additional Project Details

Registered

2009-12-02

Report inappropriate content

Auth0 B2B Essentials: SSO, MFA, and RBAC Built In

Unlimited organizations, 3 enterprise SSO connections, role-based access control, and pro MFA included. Dev and prod tenants out of the box.

Auth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.

Recommended Projects

Sideband
LXMF client for Android, Linux and macOS to communicate with people
Pear Linux - Operating system
Reliable, efficient and innovative linux OS
Chemprop
Message Passing Neural Networks for Molecule Property Prediction
Influence Maximization via Martingales
This is the code used in the experiments of the following paper: Youze Tang, Yanchen Shi, and Xiaokui Xiao. "Influence Maximization in Near-Linear Time: A Martingale Approach." Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015. Please cite the paper if you choose to use the code.
TIM_Plus
This is the code used in the experiments of the following paper: Youze Tang, Xiaokui Xiao, and Yanchen Shi. "Influence maximization: Near-optimal time complexity meets practical efficiency." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 2014. Please cite the paper if you choose to use the code.