8 research outputs found
Probing Limits of Information Spread with Sequential Seeding
We consider here information spread which propagates with certain probability
from nodes just activated to their not yet activated neighbors. Diffusion
cascades can be triggered by activation of even a small set of nodes. Such
activation is commonly performed in a single stage. A novel approach based on
sequential seeding is analyzed here resulting in three fundamental
contributions. First, we propose a coordinated execution of randomized choices
to enable precise comparison of different algorithms in general. We apply it
here when the newly activated nodes at each stage of spreading attempt to
activate their neighbors. Then, we present a formal proof that sequential
seeding delivers at least as large coverage as the single stage seeding does.
Moreover, we also show that, under modest assumptions, sequential seeding
achieves coverage provably better than the single stage based approach using
the same number of seeds and node ranking. Finally, we present experimental
results showing how single stage and sequential approaches on directed and
undirected graphs compare to the well-known greedy approach to provide the
objective measure of the sequential seeding benefits. Surprisingly, applying
sequential seeding to a simple degree-based selection leads to higher coverage
than achieved by the computationally expensive greedy approach currently
considered to be the best heuristic
Extraction of Multi-layered Social Networks from Activity Data
The data gathered in all kind of web-based systems, which enable users to
interact with each other, provides an opportunity to extract social networks
that consist of people and relationships between them. The emerging structures
are very complex due to the number and type of discovered connections. In
webbased systems, the characteristic element of each interaction between users
is that there is always an object that serves as a communication medium. This
can be e.g. an email sent from one user to another or post at the forum
authored by one user and commented by others. Based on these objects and
activities that users perform towards them, different kinds of relationships
can be identified and extracted. Additional challenge arises from the fact that
hierarchies can exist between objects, e.g. a forum consists of one or more
groups of topics, and each of them contains topics that finally include posts.
In this paper, we propose a new method for creation of multi-layered social
network based on the data about users activities towards different types of
objects between which the hierarchy exists. Due to the flattening,
preprocessing procedure new layers and new relationships in the multi-layered
social network can be identified and analysed.Comment: 20 pages, 15 figure
Model of Multilayer Knowledge Diffusion for Competence Development in an Organization
Growing role of intellectual capital within organizations is affecting new
strategies related to knowledge management and competence development. Among
different aspects related to this field, knowledge diffusion has become one of
interesting areas from both practitioner and researchers perspective. Several
models were proposed with main goal to simulate diffusion and to explain the
nature of these processes. Existing models are focused on knowledge diffusion
and they assume diffusion within a single layer using knowledge representation.
From the organizational perspective connecting several types of knowledge and
modelling changes of competence can bring additional value. In the article we
extended existing approaches by using multilayer diffusion model and focused on
analysis of competence development process. The proposed model describes
competence development process in a new way through horizontal and vertical
knowledge diffusion in multilayer network. In the network, agents collaborate
and interchange various kind of knowledge through different layers and this
mutual activities affect the competences in a positive or negative way. Taking
under consideration workers cognitive and social abilities and the previous
level of competence the new competence level can be estimated. The model is
developed to support competence management in different organizations
Model of Multilayer Knowledge Diffusion for Competence Development in an Organization
Growing role of intellectual capital within organizations is affecting new strategies related to knowledge management and competence development. Among different aspects related to this field, knowledge diffusion has become one of the interesting areas from both practitioner and researcher鈥檚 perspectives. Several models were proposed with main goal of simulating diffusion and explaining the nature of these processes. Existing models are focused on knowledge diffusion and they assume diffusion within a single layer using knowledge representation. From the organizational perspective connecting several types of knowledge and modelling changes of competence can bring additional value. In this paper we extended existing approaches by using multilayer diffusion model and focused on analysis of competence development process. The proposed model describes competence development process in a new way through horizontal and vertical knowledge diffusion in multilayer network. In the network, agents collaborate and interchange various kinds of knowledge through different layers and these mutual activities affect the competencies in a positive or negative way. Taking into consideration worker鈥檚 cognitive and social abilities and the previous level of competence the new competence level can be estimated. The model is developed to support competence management in different organizations