Jussi M Kumpula's research while affiliated with University of Helsinki and other places

Publications (9)

Article
Full-text available
In complex network research clique percolation, introduced by Palla, Derényi, and Vicsek [Nature (London) 435, 814 (2005)], is a deterministic community detection method which allows for overlapping communities and is purely based on local topological properties of a network. Here we present a sequential clique percolation algorithm (SCP) to do fas...
Article
Lipoprotein particles are commonly known as micellar aggregates with hydrophobic lipids located within the core and amphipathic molecules in the surface. Using a new structural model for optimizing the distribution of hydrophobic lipids, namely triglyceride (TG) and cholesterol ester (CE) molecules, we reveal that particle size-dependent proportion...
Article
Full-text available
We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We d...
Article
Full-text available
Topology and weights are closely related in weighted complex networks and this is reflected in their modular structure. We present a simple network model where the weights are generated dynamically and they shape the developing topology. By tuning a model parameter governing the importance of weights, the resulting networks undergo a gradual struct...
Article
Full-text available
Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by...
Article
The structure of social networks influences dynamic processes of human interaction and communication, such as opinion formation and spreading of information or infectious diseases. To facilitate simulation studies of such processes, we have developed a weighted network model to resemble the structure of real social networks, in particular taking in...
Article
Full-text available
According to Fortunato and Barthélemy, modularity-based community detection algorithms have a resolution threshold such that small communities in a large network are invisible. Here we generalize their work and show that the q-state Potts community detection method introduced by Reichardt and Bornholdt also has a resolution threshold. The model co...
Article
The quantized mean-field method (QMF) is an extension of the traditional mean-field method for simulating quantum mechanical systems with electrons and ions. It adds the quantum nature of the ions to the simulations. Here we transform the QMF equations from wave function form to density operator form, study the connection between QMF and the recent...

Citations

... So as to mimic a real network with the properties chosen for the model, it is necessary to build a social network which can handle weighted edges, and where homophily can be incorporated to handle the changes in the node states. The work done by (Toivonen et al., 2007) is a very suitable and good inspiration for our work. They present a model which simulates real networks taking into account the theory of weighttopology correlations as their basis. ...
... It was shown by researchers [52] that calculating joule heating with traditional methods in nanoscale conductors is inaccurate [53]. Horsfield and co-workers [52,[54][55][56][57] studied the joule heating in nanoscale devices using classical, semi-classical and quantum mechanical formulations by coupling the electronic and atomic dynamics. ...
... [10]. This resolution threshold is determined by the numbers of the links in communities and networks [9,24]. Note that, the community division at each resolution level reflects a possible module function in the real-world systems [44], and these communities may be different from the real ones. ...
... Kumpula et al. [41] proposed the sequential clique percolation algorithm (SCP) for fast clique percolation in the weighted and un-weighted graph. SCP is can also be called the weighted clique percolation method. ...
... Nodes can be drugs, proteins, genes, and complexes etc. while the edges can be the interaction between them. Network edges can be directed [93][94][95], undirected [96] or weighted [97,98]. Mostly the quantitative information derived from high-throughput screening is used to construct the weighted networks where edge is represented by some numerical values. ...
... Dyslipidemia refers to abnormal levels of one or more lipids, such as plasma cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and/or plasma triglycerides (TG) in blood, leading to complex cardiometabolic diseases such as atherosclerosis, type 2 diabetes (T2D), or myocardial infarction (MI) [1][2][3][4][5]. Due to their poor solubility in blood, lipids are transported in lipoprotein particles that can be categorized according to their size, density, and composition as shown in Fig. 1 [6][7][8]. Lipoproteins are main players of the exogenous, endogenous, and reverse cholesterol transport pathways, thus contributing to lipid metabolism as illustrated in Fig. 2 [6,9]. The smallest lipid molecules contained in lipoprotein particles are saturated and unsaturated fatty acids. ...
... In the traditional community detection field, multiscale community detection is an interesting and significant issue [5,41] . Clearly, multiscale local community detection is also useful [21,32,37] . For a given node, sometimes we may want a smaller community, and sometimes, a larger one. ...
... This is typified by the resolution limit that exists in the modularity; modularity optimization may not identify modules that are smaller than a scale even when the modules are well defined [30]. Some other single objective algorithms have similar resolution limits [31]. Additionally, many single-objective algorithms demand pre-information regarding the number of communities and this is not usually known for real networks [32][33][34]. ...