Flavio Chierichetti

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Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries, we study the extent to which a large network can be compressed. Boldi and Vigna (WWW 2004), showed that Web graphs can be compressed down to three bits of storage per edge; we study the compressibility of social networks where again(More)
We show that if a connected graph with $n$ nodes has conductance &#966; then rumour spreading, also known as randomized broadcast, successfully broadcasts a message within ~O(&#966;<sup>-1</sup> &#8226; log n), many rounds with high probability, regardless of the source, by using the PUSH-PULL strategy. The ~O(&#8226;&#8226;) notation hides a polylog(More)
Constraint-based approaches recently brought new insight into our understanding of metabolism. By making very simple assumptions such as that the system is at steady-state and some reactions are irreversible, and without requiring kinetic parameters, general properties of the system can be derived. A central concept in this methodology is the notion of an(More)
The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network,(More)
Floating codes are codes designed to store multiple values in a Write Asymmetric Memory, with applications to flash memory. In this model, a memory consists of a block of <i>n</i> cells, with each cell in one of q states {0,1,...,q -1}. The cells are used to represent k variable values from an ¿-ary alphabet. Cells can move from lower values to higher(More)
Correlation clustering is a basic primitive in data miner's toolkit with applications ranging from entity matching to social network analysis. The goal in correlation clustering is, given a graph with signed edges, partition the nodes into clusters to minimize the number of disagreements. In this paper we obtain a new algorithm for correlation clustering.(More)
When individuals in a social network make decisions that depend on what others have done earlier, there is the potential for a <i>cascade</i> to form --- a run of behaviors that are highly correlated. In an arbitrary network, the outcome of such a cascade can depend sensitively on the order in which nodes make their decisions, but to do date there has been(More)