Low congestion online routing and an improved mistake bound for online prediction of graph labeling

نویسندگان

  • Jittat Fakcharoenphol
  • Boonserm Kijsirikul
چکیده

In this paper, we show a connection between a certain online low-congestion routing problem and an online prediction of graph labeling. More specifically, we prove that if there exists a routing scheme that guarantees a congestion of α on any edge, there exists an online prediction algorithm with mistake bound α times the cut size, which is the size of the cut induced by the label partitioning of graph vertices. With previous known bound of O(log n) for α for the routing problem on trees with n vertices, we obtain an improved prediction algorithm for graphs with high effective resistance. In contrast to previous approaches that move the graph problem into problems in vector space using graph Laplacian and rely on the analysis of the perceptron algorithm, our proof are purely combinatorial. Further more, our approach directly generalizes to the case where labels are not binary.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Predicting a switching sequence of graph labelings

We study the problem of predicting online the labeling of a graph. We consider a novel setting for this problem in which, in addition to observing vertices and labels on the graph, we also observe a sequence of just vertices on a second graph. A latent labeling of the second graph selects one of K labelings to be active on the first graph. We propose a polynomial time algorithm for online predi...

متن کامل

Prediction on a Graph with a Perceptron

We study the problem of online prediction of a noisy labeling of a graph with the perceptron. We address both label noise and concept noise. Graph learning is framed as an instance of prediction on a finite set. To treat label noise we show that the hinge loss bounds derived by Gentile [1] for online perceptron learning can be transformed to relative mistake bounds with an optimal leading const...

متن کامل

Online Learning a Binary Labeling of a Graph

We investigate the problem of online learning a binary labeling of the vertices for a given graph. We design an algorithm, Majority, to solve the problem and show its optimality on clique graphs. For general graphs we derive a relevant mistake bound that relates the algorithm’s performance to the cut size (the number of edges between vertices with opposite labeling) and the maximum independent ...

متن کامل

Online Composition Prediction of a Debutanizer Column Using Artificial Neural Network

The current method for composition measurement of an industrial distillation column includes an offline method, which is slow, tedious and could lead to inaccurate results. Among advantages of using online composition designed are to overcome the long time delay introduced by laboratory sampling and provide better estimation, which is suitable for online monitoring purposes. This paper pres...

متن کامل

Providing a Link Prediction Model based on Structural and Homophily Similarity in Social Networks

In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Most online social networks are growing and changing with new communications (new edges). Forecasting new edges in online social networks can give us a better understanding of the growth of these networ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/0809.2075  شماره 

صفحات  -

تاریخ انتشار 2008