Liudmila Ostroumova

Prediction of retweet cascade size over time

Retweet cascades play an essential role in information diffusion in Twitter. Popular tweets reflect the current trends in Twitter, while Twitter itself is one of the most important online media. Thus, understanding the reasons why a tweet becomes popular is of great interest for sociologists, marketers and social media researches. What is even more important is the possibility to make a prognosis of a tweets future popularity. Besides the scientific significance of such possibility, this sort of prediction has lots of practical applications such as breaking news detection, viral marketing etc. In this paper we try to forecast how many retweets a given tweet will gain during a fixed time period. We train an algorithm that predicts the number of retweets during time T since the initial moment. In addition to a standard set of features we utilize several new ones. One of the most important features is the flow of the cascade. Another one is PageRank on the retweet graph, which can be considered as the measure of influence of users.

Generalized Preferential Attachment: Tunable Power-Law Degree Distribution and Clustering Coefficient

We propose a common framework for analysis of a wide class of preferential attachment models, which includes LCD, Buckley–Osthus, Holme–Kim and many others. The class is defined in terms of constraints that are sufficient for the study of the degree distribution and the clustering coefficient. We also consider a particular parameterized model from the class and illustrate the power of our approach as follows. Applying our general results to this model, we show that both the parameter of the power-law degree distribution and the clustering coefficient can be controlled via variation of the model parameters. In particular, the model turns out to be able to reflect realistically these two quantitative characteristics of a real network, thus performing better than previous preferential attachment models. All our theoretical results are illustrated empirically.

Timely crawling of high-quality ephemeral new content

In this paper, we study the problem of timely finding and crawling of \textit{ephemeral} new pages, i.e., for which user traffic grows really quickly right after they appear, but lasts only for several days (e.g., news, blog and forum posts). Traditional crawling policies do not give any particular priority to such pages and may thus crawl them not quickly enough, and even crawl already obsolete content. We thus propose a new metric, well thought out for this task, which takes into account the decrease of user interest for ephemeral pages over time. We show that most ephemeral new pages can be found at a relatively small set of content sources and suggest a method for finding such a set. Our idea is to periodically recrawl content sources and crawl newly created pages linked from them, focusing on high-quality (in terms of user interest) content. One of the main difficulties here is to divide resources between these two activities in an efficient way. We find the adaptive balance between crawls and recrawls by maximizing the proposed metric. Further, we incorporate search engine click logs to give our crawler an insight about the current user demands. The effectiveness of our approach is finally demonstrated experimentally on real-world data.

Evolution of the Media Web

We present a detailed study of the part of the Web related to media content, i.e., the Media Web. Using publicly available data, we analyze the evolution of incoming and outgoing links from and to media pages. Based on our observations, we propose a new class of models for the appearance of new media content on the Web where different \textit{attractiveness} functions of nodes are possible including ones taken from well-known preferential attachment and fitness models. We analyze these models theoretically and empirically and show which ones realistically predict both the incoming degree distribution and the so-called \textit{recency property} of the Media Web, something that existing models did not do well. Finally we compare these models by estimating the likelihood of the real-world link graph from our data set given each model and obtain that models we introduce are significantly more likely than previously proposed ones. One of the most surprising results is that in the Media Web the probability for a post to be cited is determined, most likely, by its quality rather than by its current popularity.

The Distribution of Second Degrees in the Buckley–Osthus Random Graph Model

In this article we consider a well-known generalization of the Barabási and Albert preferential attachment model—the Buckley–Osthus model. Buckley and Osthus proved that in this model, the degree sequence has a power law distribution. As a natural (and arguably more interesting) next step, we study the second degrees of vertices. Roughly speaking, the second degree of a vertex is the number of vertices at distance two from the given vertex. The distribution of second degrees is of interest because it is a good approximation of PageRank, where the importance of a vertex is measured by taking into account the popularity of its neighbors. We prove that the second degrees also obey a power law. More precisely, we estimate the expectation of the number of vertices with the second degree greater than or equal to k and prove the concentration of this random variable around its expectation using the now-famous Talagrands concentration inequality over product spaces. As far as we know, this is the only application of Talagrands inequality to random web graphs where the (preferential attachment) edges are not defined over a product distribution, making the application nontrivial and requiring a certain degree of novelty.