- Modeling Viral Information Spreading via Directed Acyclic Graph Diffusion(arXiv)
Abstract : iral information like rumors or fake news is spread over a communication network like a virus infection in a unidirectional manner: entity i conveys information to a neighbor j, resulting in two equally informed (infected) parties. Existing graph diffusion works focus only on bidirectional diffusion on an undirected graph. Instead, we propose a new directed acyclic graph (DAG) diffusion model to estimate the probability xi(t) of node i’s infection at time t given a source node s, where xi(∞) = 1. Specifically, given an undirected positive graph modeling node-to-node communication, we first compute its graph embedding: a latent coordinate for each node in an assumed low-dimensional manifold space from extreme eigenvectors via LOBPCG. Next, we construct a DAG based on Euclidean distances between latent coordinates. Spectrally, we prove that the asymmetric DAG Laplacian matrix contains real non-negative eigenvalues, and that the DAG diffusion converges to the all-infection vector $x(infty) = 1$ as t→∞. Simulation experiments show that our proposed DAG diffusion accurately models viral information spreading over a variety of graph structures at different time instants.
2.Grooming Connectivity Intents in IP-Optical Networks Using Directed Acyclic Graphs (arXiv)
Abstract : During the last few years, there have been concentrated efforts toward intent-driven networking. While relying upon Software-Defined Networking (SDN), Intent-Based Networking (IBN) pushes the frontiers of efficient networking by decoupling the intentions of a network operator (i.e., what is desired to be done) from the implementation (i.e., how is it achieved). The advantages of such a paradigm have long been argued and include, but are not limited to, the reduction of human errors, reduced expertise requirements among operator personnel, and faster business plan adaptation. In previous work, we have shown how incorporating IBN in multi-domain networks can have a significantly positive impact as it can enable decentralized operation, accountability, and confidentiality. The pillar of our previous contribution is the compilation of intents using system-generated intent trees. In this work, we extend the architecture to enable grooming among the user intents. Therefore, separate intents can now end up using the same network resources. While this makes the intent system reasonably more complex, it indisputably improves resource allocation. To represent the intent relationships of the newly enhanced architecture, we use Directed Acyclic Graphs (DAGs). Furthermore, we appropriately adapt an advanced established technique from the literature to solve the Routing, Modulation, and Spectrum Assignment (RMSA) problem for the intent compilation. We demonstrate a realistic scenario in which we evaluate our architecture and the intent compilation strategy. Our current approach successfully consolidates the advantages of having an intent-driven architecture and, at the same time, flexibly choosing among advanced resource allocation techniques.