Biomedical subjects

Santo Fortunato

Publications and source records attributed to Santo Fortunato.

6 recordsLinked to original sources

PubMed2006

Resolution limit in community detection.

Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.

Animals↗

PubMed2006

Scale-free network growth by ranking.

Network growth is currently explained through mechanisms that rely on node prestige measures, such as degree or fitness. In many real networks, those who create and connect nodes do not know the prestige values of existing nodes but only their ranking by prestige. We propose a criterion of network growth that explicitly relies on the ranking of the nodes according to any prestige measure, be it topological or not. The resulting network has a scale-free degree distribution when the probability to link a target node is any power-law function of its rank, even when one has only partial information of node ranks. Our criterion may explain the frequency and robustness of scale-free degree distributions in real networks, as illustrated by the special case of the Web graph.

Journal Article↗

PubMed2005

Phase III randomized trial of FOLFIRI versus FOLFOX4 in the treatment of advanced colorectal cancer: a multicenter study of the Gruppo Oncologico Dell'Italia Meridionale.

PURPOSE: We performed this phase III study to compare the irinotecan, leucovorin (LV), and fluorouracil (FU) regimen (FOLFIRI) versus the oxaliplatin, LV, and FU regimen (FOLFOX4) in previously untreated patients with advanced colorectal cancer. PATIENTS AND METHODS: A total of 360 chemotherapy-naive patients were randomly assigned to receive, every 2 weeks, either arm A (FOLFIRI: irinotecan 180 mg/m(2) on day 1 with LV 100 mg/m(2) administered as a 2-hour infusion before FU 400 mg/m(2) administered as an intravenous bolus injection, and FU 600 mg/m(2) as a 22-hour infusion immediately after FU bolus injection on days 1 and 2 [LV5FU2]) or arm B (FOLFOX4: oxaliplatin 85 mg/m(2) on day 1 with LV5FU2 regimen). RESULTS: One hundred sixty-four and 172 patients were assessable in arm A and B, respectively. Overall response rates (ORR) were 31% in arm A (95% CI, 24.6% to 38.3%) and 34% in arm B (95% CI, 27.2% to 41.5%; P = .60). In both arms A and B, median time to progression (TTP; 7 v 7 months, respectively), duration of response (9 v 10 months, respectively), and overall survival (OS; 14 v 15 months, respectively) were similar, without any statistically significant difference. Toxicity was mild in both groups: alopecia and gastrointestinal disturbances were the most common toxicities in arm A; thrombocytopenia and neurosensorial were the most common toxicities in arm B. Grade 3 to 4 toxicities were uncommon in both arms, and no statistical significant difference was observed. CONCLUSION: There is no difference in ORR, TTP, and OS for patients treated with the FOLFIRI or FOLFOX4 regimen. Both therapies seemed effective as first-line treatment in these patients. The difference between these two combination therapies is mainly in the toxicity profile.

Adult↗

PubMed2005

Importance of extremists for the structure of social networks.

The structure and the properties of complex networks essentially depend on the way nodes get connected to each other. We assume here that each node has a feature which attracts the others. We model the situation by assigning two numbers to each node, omega and alpha, where omega indicates some property of the node and alpha the affinity towards that property. A node A is more likely to establish a connection with a node B if B has a high value of omega and A has a high value of alpha. Simple computer simulations show that networks built according to this principle have a degree distribution with a power-law tail, whose exponent is determined only by the nodes with the largest value of the affinity alpha (the "extremists"). This means that the extremists lead the formation process of the network and manage to shape the final topology of the system. The latter phenomenon may have implications in the study of social networks and in epidemiology.

Journal Article↗

PubMed2004

Number of spanning clusters at the high-dimensional percolation thresholds.

A scaling theory is used to derive the dependence of the average number k of spanning clusters at threshold on the lattice size L. This number should become independent of L for dimensions d<6 and vary as ln L at d=6 . The predictions for d>6 depend on the boundary conditions, and the results there may vary between L(d-6) and L0. While simulations in six dimensions are consistent with this prediction [after including corrections of order ln(ln L)], in five dimensions the average number of spanning clusters still increases as ln L even up to L=201 . However, the histogram P(k) of the spanning cluster multiplicity does scale as a function of kX(L), with X(L) =1+const/L, indicating that for sufficiently large L the average k will approach a finite value: a fit of the five-dimensional multiplicity data with a constant plus a simple linear correction to scaling reproduces the data very well. Numerical simulations for d>6 and for d=4 are also presented.

Journal Article↗

PubMed2004

Method to find community structures based on information centrality.

Community structures are an important feature of many social, biological, and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries [M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)]. We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n4) , is very effective especially when the communities are very mixed and hardly detectable by the other methods.

Algorithms↗