ART-naive HIV patients at Feleg-Hiwot Referral Hospital Northwest, Ethiopia

Objectives: To determine socio-demographic and immunological status of anti-retroviral treatment (ART)-naïve HIVpositive patients.Methods: This was a longitudinal survey of HIV-positive patients treated with ART at Felege-Hiwot Hospital. CD4 cell counts were enumerated at baseline and after 6 months of treatment using FACS count (Becton Dickinson). Socioeconomic data were collected using pre tested questionnaires.Results: Three hundred sixty eight (62% female), with median age 30 years were enrolled. Of these, 207 (56.5%) were uneducated and 233 (66.8%) had monthly income ≤ 250 birr. Three hundred fifteen (85.6%) started ART within 6 months of HIV diagnosis. The mean (95% CI) CD4 cell count at baseline was 153 (139-167); 156 (137-175) for females and 122 cells/μl (105-139) for males (

Un algorithme de test pour la connexit\'e temporelle des graphes dynamiques de faible densit\'e

We address the problem of testing whether a dynamic graph is temporally connected, i.e. a temporal path ({\em journey}) exists between all pairs of vertices. We consider a discrete version of the problem, where the topology is given as an evolving graph \G=\{G_1,G_2,...,G_{k}\} in which only the set of (directed) edges varies. Two cases are studied, depending on whether a single edge or an unlimited number of edges can be crossed in a same $G_i$ (strict journeys {\it vs} non-strict journeys). For strict journeys, two existing algorithms designed for other problems can be adapted. However, we show that a dedicated approach achieves a better time complexity than one of these two algorithms in all cases, and than the other one for those graphs whose density is low at any time (though arbitrary over time). The time complexity of our algorithm is $O(k\mu n)$, where k=|\G| is the number of time steps and $\mu=max(|E_i|)$ is the maximum {\em instant} density, to be contrasted with $m=|\cup E_i|$, the {\em cumulated} density. Indeed, it is not uncommon for a mobility scenario to satisfy, for instance, both $\mu=o(n)$ and $m=\Theta(n^2)$. We characterize the key values of $k, \mu$ and $m$ for which our algorithm should be used. For non-strict journeys, for which no algorithm is known, we show that a similar strategy can be used to answer the question, still in $O(k\mu n)$ time

Calcul de paramètres minimaux dans les graphes dynamiques.

International audienceDans un travail précédent [1], nous avons présenté un algorithme permettant de calculer un paramètre appelé T -interval connectivity dans les graphes dynamiques qui se présentent sous forme d’une suite de graphes G 1 , G 2 , ..., G δ . Cet algorithme considère les graphes de la suite comme des atomes et les manipule via deux opérations élémentaires : une opération de composition (de deux graphes) et une opération de test (sur un graphe). Cet algorithme est optimal dans le sens où il n’utilise que O(δ) opérations de ce type. Dans cet article, nous généralisons cette approche, en montrant notamment qu’il suffit de définir différemment les opérations de composition et de test pour résoudre immédiatement d’autres problèmes. Nous illustrons cela par l’étude de trois problèmes de minimisation, à savoir BOUNDED-REALIZATION-OF-THE-FOOTPRINT, TEMPORAL-DIAMETER, et ROUND-TRIP-TEMPORAL-DIAMETER, chacun faisant référence à une propriété temporelle importante dans les réseaux dynamiques.[1] A. Casteigts, R. Klasing, Y. M. Neggaz, J. G. Peters. Tester efficacement la T-intervalle connexité dans les graphesdynamiques. CIAC 2015 (English) and ALGOTEL 2015 (French)

Efficiently Testing T-Interval Connectivity in Dynamic Graphs

International audienc

Maintaining a Distributed Spanning Forest in Highly Dynamic Networks

International audienceHighly dynamic networks are characterized by frequent changes in the availability of communication links. These networks are often partitioned into several components, which split and merge unpredictably. We present a distributed algorithm that maintains a forest of (as few as possible) spanning trees in such a network, with no restriction on the rate of change. Our algorithm is inspired by high-level graph transformations, which we adapt here in a (synchronous) message passing model for dynamic networks. The resulting algorithm has the following properties. First, every decision is purely local-in each round, a node only considers its role and that of its neighbors in the tree, with no further information propagation (in particular, no wave mechanisms). Second, whatever the rate and scale of the changes, the algorithm guarantees that, by the end of every round, the network is covered by a forest of spanning trees in which 1) no cycle occur, 2) every node belongs to exactly one tree, and 3) every tree contains exactly one root. We primarily focus on the correctness of this algorithm, which is established rigorously. While performance is not the main focus, we suggest new complexity metrics for such problems, and report on preliminary experimentation results validating our algorithm in a practical scenario

HGExplainer: Explainable Heterogeneous Graph Neural Network

International audienceGraph Neural Networks (GNNs) are an effective framework for graph representation learning in real-world appli- cations. However, despite their increasing success, they remain notoriously challenging to interpret, and their predictions are hard to explain. Nowadays, several recent works have proposed methods to explain the decisions made by GNNs. However, they only aggregate information from the same type of neighbors or indiscriminately treat homogeneous and heterogeneous neighbors similarly. Based on these observations, we propose HGExplainer, an explainer for heterogeneous GNNs to comprehensively capture structural, semantic, and attribute information from homogeneous and heterogeneous neighbors. We first train the GNN model to represent the predictions on a heterogeneous network. To make the explainable predictions, we design the model to capture heterogeneity information in calculating the joint mutual information maximization, extracting the meta-path-based graph sampling to generate more prosperous and more accurate explanations. Finally, we evaluate our explainable method on synthetic and real-life datasets and perform concrete case studies. Extensive results show that HGExplainer can provide inherent explanations while achieving high accuracy

Maintaining a Distributed Spanning Forest in Highly Dynamic Networks

International audienceHighly dynamic networks are characterized by frequent changes in the availability of communication links. These networks are often partitioned into several components, which split and merge unpredictably. We present a distributed algorithm that maintains a forest of (as few as possible) spanning trees in such a network, with no restriction on the rate of change. Our algorithm is inspired by high-level graph transformations, which we adapt here in a (synchronous) message passing model for dynamic networks. The resulting algorithm has the following properties. First, every decision is purely local-in each round, a node only considers its role and that of its neighbors in the tree, with no further information propagation (in particular, no wave mechanisms). Second, whatever the rate and scale of the changes, the algorithm guarantees that, by the end of every round, the network is covered by a forest of spanning trees in which 1) no cycle occur, 2) every node belongs to exactly one tree, and 3) every tree contains exactly one root. We primarily focus on the correctness of this algorithm, which is established rigorously. While performance is not the main focus, we suggest new complexity metrics for such problems, and report on preliminary experimentation results validating our algorithm in a practical scenario