Many systems, including the Internet, social networks, and the power grid, can be represented as graphs. When analyzing graphs, it is often useful to compute scores describing the relative importance or distance between nodes. One example is Personalized PageRank (PPR), which assigns to each node v a vector whose i-th entry describes the importance of the i-th node from the perspective of v. PPR has proven useful in many applications, such as recommending who users should follow on social networks (if this i-th entry is large, v may be interested in following the i-th user). Unfortunately, computing n such PPR vectors (where n is the number of nodes) is infeasible for many graphs of interest.

In this work, we argue that the situation is not so dire. Our main result shows that the dimensionality of the set of PPR vectors scales sublinearly in n with high probability, for a certain class of random graphs and for a notion of dimensionality similar to rank. Put differently, we argue that the effective dimension of this set is much less than n, despite the fact that the matrix containing these vectors has rank n. Furthermore, we show this dimensionality measure relates closely to the complexity of a PPR estimation scheme that was proposed (but not analyzed) by Jeh and Widom. This allows us to argue that accurately estimating all n PPR vectors amounts to computing a vanishing fraction of the n^2 vector elements (when the technical assumptions of our main result are satisfied). Finally, we demonstrate empirically that similar conclusions hold when considering real-world networks, despite the assumptions of our theory not holding.

This is joint work with Daniel Vial, University of Michigan and will appear in ACM Sigmetrics 2019.

**Bio: **

**Vijay Subramanian** is an Associate Professor in the EECS Department at the University of Michigan since 2014. After graduating with his Ph.D. from UIUC in 1999, he did a few stints in industry, research institutes and universities in the US and Europe before his current position . His main research interests are in stochastic modeling, communications, information theory and applied mathematics. A large portion of his past work has been on probabilistic analysis of communication networks, especially analysis of scheduling and routing algorithms. In the past he has also done some work with applications in immunology and coding of stochastic processes. His current research interests are on game theoretic and economic modeling of socio-technological systems and networks, and the analysis of associated stochastic processes.