We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of weblogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.

This paper presents the architecture of PIER , an Internetscale query engine we have been building over the last three years. PIER is the first general-purpose relational query processor targeted at a peer-to-peer (p2p) architecture of thousands or millions of participating nodes on the Internet. It supports massively distributed, database-style dataflows for snapshot and continuous queries. It is intended to serve as a building block for a diverse set of Internet-scale informationcentric applications, particularly those that tap into the standardized data readily available on networked machines, including packet headers, system logs, and file names

northwestern.edu We discuss the use of social networks in implementing viral marketing strategies. While influence maximization has been studied in this context (see Chapter 24 of [10]), we study revenue maximization, arguably, a more natural objective. In our model, a buyer’s decision to buy an item is influenced by the set of other buyers that own the item and the price at which the item is offered. We focus on algorithmic question of finding revenue maximizing marketing strategies. When the buyers are completely symmetric, we can find the optimal marketing strategy in polynomial time. In the general case, motivated by hardness results, we investigate approximation algorithms for this problem. We identify a family of strategies called influence-and-exploit strategies that are based on the following idea: Initially influence the population by giving the item for free to carefully a chosen set of buyers. Then extract revenue from the remaining buyers using a ‘greedy ’ pricing strategy. We first argue why such strategies are reasonable and then show how to use recently developed set-function maximization techniques to find the right set of buyers to influence.

We present an algorithm for solving a broad class of online resource allocation jobs arrive one at a time, and one can complete the jobs by investing time in a number of abstract activities, according to some schedule. We assume that the fraction of jobs completed by a schedule is a monotone, submodular function of a set of pairs (v, τ), where τ is the time invested in activity v. Under this assumption, our online algorithm performs near-optimally according to two natural metrics: (i) the fraction of jobs completed within time T, for some fixed deadline T> 0, and (ii) the average time required to complete each job. We evaluate our algorithm experimentally by using it to learn, online, a schedule for allocating CPU time among solvers entered in the 2007 SAT solver competition. 1

Information diffusion, viral marketing, and collective classification all attempt to model and exploit the relationships in a network to make inferences about the labels of nodes. A variety of techniques have been introduced and methods that combine attribute information and neighboring label information have been shown to be effective for collective labeling of the nodes in a network. However, in part because of the correlation between node labels that the techniques exploit, it is easy to find cases in which, once a misclassification is made, incorrect information propagates throughout the network. This problem can be mitigated if the system is allowed to judiciously acquire the labels for a small number of nodes. Unfortunately, under relatively general assumptions, determining the optimal set of labels to acquire is intractable. Here we propose an acquisition method that learns the cases when a given collective classification algorithm makes mistakes, and suggests acquisitions to correct those mistakes. We empirically show on both real and synthetic datasets that this method significantly outperforms a greedy approximate inference approach, a viral marketing approach, and approaches based on network structural measures such as node degree and network clustering. In addition to significantly improving accuracy with just a small amount of labeled data, our method is tractable on large networks.

Number: CR-PRL-2008-08-0001

The rise of social media sites — blogs, wikis, and Digg — underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media have lead to a new paradigm for interacting with information: social information processing. We study how the social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show that social networks play an important role in document recommendation. The second contribution of this paper consists of a mathematical model that describes how collaborative evaluation of documents emerges from the independent decisions made by many users. The model takes into account users behavior: e.g., whether they are reading stories on the front page or through a Friends interface. Solutions of the model reproduce the observed ratings received by actual stories on Digg. 1

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Motivated by applications in social networks, peer-to-peer and overlay networks, we define and study the Bounded Budget Connection (BBC) game- we have a collection of n players or nodes each of whom has a budget for purchasing links; each link has a cost as well as a length and each node has a set of preference weights for each of the remaining nodes; the objective of each node is to use its budget to buy a set of outgoing links so as to minimize its sum of preference-weighted distances to the remaining nodes. We study the structural and complexity-theoretic properties of pure Nash equilibria in BBC games. We show that determining the existence of a pure Nash equilibrium in general BBC games is NP-hard. We counterbalance this result by considering a natural variant, fractional BBC games- where it is permitted to buy fractions of links- and show that a pure Nash equilibrium always exists in such games. A major focus is the study of (n, k)-uniform BBC games- those in which all link costs, link lengths and preference weights are equal (to 1) and all budgets are equal (to k). We show that a pure Nash equilibrium or stable graph exists for all (n, k)-uniform BBC games and that all stable graphs are essentially fair (i.e. all nodes have similar costs). We provide an explicit construction of a family of stable graphs that spans the spectrum from minimum total social cost to maximum total social cost. To be precise we show that that the price of stability is Θ(1) and the price of anarchy is Ω( n/k) and O( logk n

Abstract. In this paper we consider the problem of learning hidden independent cascade social networks using exact value injection queries. These queries involve activating and suppressing agents in the target network. We develop an algorithm that optimally learns an arbitrary social network of size n using O(n 2) queries, matching the information theoretic lower bound we prove for this problem. We also consider the case when the target social network forms a tree and show that the learning problem takes Θ(n log(n)) queries. We also give an approximation algorithm for finding an influential set of nodes in the network, without resorting to learning its structure. Finally, we discuss some limitations of our approach, and limitations of path-based methods, when non-exact value injection queries are used. 1