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- giant component (2)
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Project
- A5 (22)
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- A (22)
Random intersection graphs naturally exhibit a certain amount of transitivity and hence can
be used to model real--world networks. We study the evolution of the chromatic number
of a random intersection graph and show that, in a certain range of parameters,
these random graphs can be coloured optimally with high probability using different greedy
algorithms.
Experiments on real network data confirm the positive theoretical predictions and
suggest that heuristics for the clique and the chromatic number can work hand in hand
proving mutual optimality.
Let H_d(n, p) signify a random d-uniform hypergraph with n vertices in which each of the possible edges is present with probability p = p(n) independently, and let H_d(n,m) denote a uniformly distributed d-uniform hypergraph with n vertices and m edges. We derive local limit theorems for the joint distribution of the number of vertices and the number of edges in the largest component of H_d(n, p) and H_d(n,m). As an application, we obtain an asymptotic formula for the probability that H_d(n, p) is connected, and a corresponding formula for H_d(n,m). In addition, we infer a local limit theorem for the conditional distribution of the number of edges in H_d(n, p) given that H_d(n, p) is connected. While most prior work on this subject relies on techniques from enumerative combinatorics, we present a new, purely probabilistic approach.
In a database of about 2000 approved drugs, represented by 105 structural conformers,
we have performed 2D comparisons (Tanimoto coefficients) and 3D superpositions. For one class of drugs the correlation
between structural resemblance and similar action was analysed in detail.
In general Tanimoto cofficients and 3D scores give similar results, but we
find that 2D similarity measures neglect important structural/funtional
features. Examples for both over- and underestimation of similarity by
2D metrics are discussed. The required additional effort for 3D superpositions
is assessed by implementation of a fast algorithm with a processing
time below 0:01 seconds and a more sophisticated approach (0:5 seconds
per superposition). According to the improvement of similarity detection
compared to 2D screening and the pleasant rapidity on a desktop PC,
full{atom 3D superposition will be an upcoming method of choice for
library prioritization or similarity screening approaches.
The weighted matching problem is to find a matching in a weighted graph
that has maximum weight. The fastest known algorithm for this problem has running time
O(nm +n2 log n). Many real world problems require graphs of such large size that this running
time is too costly. We present a linear time approximation algorithm for the weighted
matching problem with a performance ratio of 2
3 ???? ". This improves the previously best
performance ratio of 1
2 .
Recently two different linear time approximation algorithms for the weighted matching problem in graphs have been suggested [5][17]. Both these algorithms have a performance ratio of 1/2. In this paper we present a set of local improvement operations and prove that it guarantees a performance ratio of 2/3. We show that a maximal set of these local improvements can be found in linear time.
To see how these local improvements behave in practice we conduct an experimental comparison of four different approximation algorithms for calculating maximum weight matchings in weighted graphs. One of these algorithms is the commonly used Greedy algorithm which achieves a performance ratio of 1/2 but has O(m log n) runtime. The other three algorithms all have linear runtime. Two of them are the above mentioned 1/2 approximation algorithms. The third algorithm may have an arbitrarily bad performance ratio but in practice produces reasonably good results. We compare the quality of the algorithms on a test set of weighted graphs and study the improvement achieved by our local improvement operations. We also do a comparison of the runtimes of all algorithms.
We present a linear time approximation algorithm with a performance ratio of 1/2 for finding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [STACS'99, Lecture Notes in Comput. Sci., Vol. 1563, 1999, pp. 259–269]. Our algorithm uses a new approach which is much simpler than the one given by Preis and needs no amortized analysis for its running time.
We investigate the problem of colouring random graphs G ? G(n; p)
in polynomial expected time. For the case p ? 1.01/n, we present an algorithm
that finds an optimal colouring in linear expected time. For
p ?? ln6(n)/n, we give algorithms which approximate the chromatic
number within a factor of O(? np). We also obtain an O(?
np/ ln(np))-
approximation algorithm for the independence number. As an application,
we propose an algorithm for deciding satisfiability of random 2k-
SAT formulas (with sufficiently many clauses) in
polynomial expected time.