172,082 research outputs found
Community Deception in Networks: Where We Are and Where We Should Go
No full textCommunity deception tackles the following problem: given a target community C inside a network G and a budget of updates β (e.g., edge removal and additions), what is the best way (i.e., optimization of some function φG(C)) to perform such updates in a way that C can escape to a detector D (i.e., a community detection algorithm)? This paper aims at: (i) presenting an analysis of the state-of-the-art deception techniques; (ii) evaluating state-of-the-art deception techniques: (iii) making available a library of techniques to practitioners and researchers
Web maps and their algebra
No full textA map is an abstract visual representation of a region, taken from a given space, usually designed for final human consumption. Traditional cartography focuses on the mapping of Euclidean spaces by using some distance metric. In this paper we aim at mapping the Web space by leveraging its relational nature. We introduce a general mathematical framework for maps and an algebra. Finally, we discuss the feasibility of maps suitable for interpretation not only by humans but also by machines
Knowledge maps of Web graphs
No full textIn this short note we give an overview of our research concerning cartography on the Web and its challenges. We present a mathematical formalism to capture the notion of map on theWeb, which allows to automatize the construction of maps
Extracting relevant subgraphs from graph navigation
No full textThe main goal of current Web navigation languages is to retrieve set of nodes reachable from a given node. No information is provided about the fragments of the Web navigated to reach these nodes. In other words, information about their connections is lost. This paper presents an efficient algorithm to extract relevant parts of these Web fragments and shows the importance of producing subgraphs besides of sets of nodes. We discuss examples with real data using an implementation of the algorithm in the EXpRESs tool
Role of distinct natural killer cell subsets in anticancer response
No full textNatural killer (NK) cells, the prototypic member of innate lymphoid cells, are important
effectors of anticancer immune response. These cells can survey and control tumor
initiation due to their capability to recognize and kill malignant cells and to regulate the
adaptive immune response via cytokines and chemokines release. However, several
studies have shown that tumor-infiltrating NK cells associated with advanced disease can
have profound functional defects and display protumor activity. This evidence indicates
that NK cell behavior undergoes crucial alterations during cancer progression. Moreover,
a further level of complexity is due to the extensive heterogeneity and plasticity of these
lymphocytes, implying that different NK cell subsets, endowed with specific phenotypic
and functional features, may be involved and play distinct roles in the tumor context.
Accordingly, many studies reported the enrichment of selective NK cell subsets within
tumor tissue, whereas the underlying mechanisms are not fully elucidated. A malignant
microenvironment can significantly impact NK cell activity, by recruiting specific subpopulations
and/or influencing their developmental programming or the acquisition of a
mature phenotype; in particular, neoplastic, stroma and immune cells, or tumor-derived
factors take part in these processes. In this review, we will summarize and discuss the
recently acquired knowledge on the possible contribution of distinct NK cell subsets in
the control and/or progression of solid and hematological malignancies. Moreover, we
will address emerging evidence regarding the role of different components of tumor
microenvironment on shaping NK cell response
Community Deception or: How to Stop Fearing Community Detection Algorithms
No full textIn this paper, we research the community deception problem. Tackling this problemconsists in developing techniques to hide a target community (C) fromcommunity detection algorithms. This need emerges whenever a group (e.g., activists, police enforcements, or network participants in general) want to observe and cooperate in a social networkwhile avoiding to be detected. We introduce and formalize the community deception problemand devise an efficient algorithmthat allows to achieve deception by identifying a certain number (beta) of C's members connections to be rewired. Deception can be practically achieved in social networks like Facebook by friending or unfriending networkmembers as indicated by our algorithm. We compare our approachwith another technique based onmodularity. By considering a variety of (large) real networks, we provide a systematic evaluation of the robustness of community detection algorithms to deception techniques. Finally, we open some challenging research questions about the design of detection algorithms robust to deception techniques
Community Deception in Weighted Networks
No full textTechniques to hide a community from community detection algorithms are emerging as a new way to protect the privacy of users.
Existing techniques either adapt optimization criteria derived from community detection (e.g., minimizing instead of maximizing modularity) or define new ones (e.g., community safeness) to identify a
set of updates (e.g., edge addition/deletions) that deceive community detection algorithms from recovering the original structure
of a target community C. However, all existing approaches do not take into account the fact that network’s edges can be weighted to
take into account node similarity of relation strength. The goal of this paper is to present Secretorum, a novel community deception
approach for community deception in weighted networks
Community Deception in Attributed Networks
No full textCommunity detection algorithms that analyze networks to identify communities of nodes are an essential part of the
network analysis toolkit used daily by different analysts (e.g., data scientists and law enforcement). However, there is not enough
awareness that members of a community C (either revealed or not) inside a network G could act strategically to evade such tools either
for legitimate (e.g., activist groups in authoritarian regimes) or malicious (e.g., terrorists) purpose. Community deception offers this
possibility. By identifying a certain number of C’s member connections to be rewired, community deception algorithms can successfully
hide a community that wants to stay below the radar of detection techniques. However, state-of-the-art deception approaches have
focused on networks without attributes, although real-world networks (e.g., Facebook) include attributes (e.g., age, sex) that play a
central role in detecting more accurate communities. This paper faces three novel challenges introduced when designing deception
techniques for networks with attributes. The first concerns how to model and encode attributes most flexibly. The second is about
framing attribute-aware community deception as an optimization problem. Finally, the challenge of solving the optimization problem by
leveraging network topology and attributes also arises. We leverage a simple way to model network attributes as edge weights, a novel
optimization function called community diffusion, and DIFFUSER a greedy algorithm to optimize diffusion, to solve the above
challenges. We evaluated DIFFUSER against several community detection algorithms and compared it with state-of-the-art deception
approaches on various real-world networks. From the evaluation, we can draw two main observations. First, adopting
attribute-oblivious deception techniques leads to unsatisfactory results. Second, community diffusion as an optimization function
specific to attributed networks is preferred to community safeness, the state-of-the-art deception optimization function, even when
recasting the latter as an attribute-aware functio
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Hyperthermia enhances CD95-ligand gene expression in T lymphocytes
No full textHyperthermia represents an interesting therapeutic strategy for the treatment of tumors. Moreover, it is able to regulate several aspects of the immune response. Fas (APO-1/CD95) and its ligand (FasL) are cell surface proteins whose interaction activates apoptosis of Fas-expressing targets. In T cells, the Fas-Fas-L system regulates activation-induced cell death, is implicated in diseases in which lymphocyte homeostasis is compromised, and plays an important role during cytotoxic and regulatory actions mediated by these cells. In this study we describe the effect of hyperthermia on activation of the fas-L gene in T lymphocytes. We show that hyperthermic treatment enhances Fas-L-mediated cytotoxicity,fas-L mRNA expression, and fas-L promoter activity in activated T cell lines. Our data indicate that hyperthermia enhances the transcriptional activity of AP-1 and NF-kappaB in activated T cells, and this correlates with an increased expression/nuclear translocation of these transcription factors. Moreover, we found that heat shock factor-1 is a transactivator of fas-L promoter in activated T cells, and the overexpression of a dominant negative form of heat shock factor-1 may attenuate the effect of hyperthermia on fas-L promoter activity. Furthermore, overexpression of dominant negative mutants of protein kinase Cepsilon (PKCepsilon) and PKCtheta partially inhibited the promoter activation and, more importantly, could significantly reduce the enhancement mediated by hyperthermia, indicating that modulation of PKC activity may play an important role in this regulation. These results add novel information on the immunomodulatory action of heat, in particular in the context of its possible use as an adjuvant therapeutic strategy to consider for the treatment of cancer
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