No abstract

In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter.For global PageRank, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. We show that with a reset probability of , the expected total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is O( n ln m 2 ). This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs Ω( ) total time and the Monte Carlo method needs O(mn/) total time; both are prohibitively expensive. We also show that we can handle deletions equally efficiently.We then study the computation of the top k personalized PageRanks starting from a seed node, assuming that personalized PageRanks follow a power-law with exponent α < 1. We show that if we store R > q ln n random walks starting from every node for large enough constant q (using the approach outlined for global PageRank), then the expected number of calls made to the distributed social network database is O(k/(R (1−α)/α )). We also present experimental results from the social networking site, Twitter, ver- * Work done while interning at Twitter † Work done while at Twitter.

With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges: (1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies are inadequate in ranking relational outputs. In this paper, we propose a novel IR ranking strategy for effective keyword search. We are the first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search company. Experimental results show that our strategy is significantly better than existing strategies. Our approach can be used both at the application level and be incorporated into a RDBMS to support keyword-based search in relational databases.

This paper examines the impact of exchange rate volatility on the trade flows of the G-7 countries in the context of a multivariate error-correction model. The errorcorrection models do not show any sign of parameter instability. The results indicate that the exchange rate volatility has a significant negative impact on the volume of exports in each of the G-7 countries. Assuming market participants are risk averse, these results imply that exchange rate uncertainty causes them to reduce their activities, change prices, or shift sources of demand and supply in order to minimize their exposure to the effects of exchange rate volatility. This, in turn, can change the distribution of output across many sectors in these countries. It is quite possible that the surprisingly weak relationship between trade flows and exchange rate volatility reported in several previous studies are due to insufficient attention to the stochastic properties of the relevant time series.