GOSSIP ON WEIGHTED NETWORKS

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.

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Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks

Applied Sciences ◽

10.3390/app11094005 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4005

Author(s):

Asep Maulana ◽

Martin Atzmueller

Keyword(s):

Anomaly Detection ◽

Complex Networks ◽

Pareto Front ◽

Scoring Function ◽

Cyber Physical Systems ◽

Complex Interaction ◽

Interaction Networks ◽

Physical Systems ◽

Human Interactions ◽

Different Levels

Anomaly detection in complex networks is an important and challenging task in many application domains. Examples include analysis and sensemaking in human interactions, e.g., in (social) interaction networks, as well as the analysis of the behavior of complex technical and cyber-physical systems such as suspicious transactions/behavior in financial or routing networks; here, behavior and/or interactions typically also occur on different levels and layers. In this paper, we focus on detecting anomalies in such complex networks. In particular, we focus on multi-layer complex networks, where we consider the problem of finding sets of anomalous nodes for group anomaly detection. Our presented method is based on centrality-based many-objective optimization on multi-layer networks. Starting from the Pareto Front obtained via many-objective optimization, we rank anomaly candidates using the centrality information on all layers. This ranking is formalized via a scoring function, which estimates relative deviations of the node centralities, considering the density of the network and its respective layers. In a human-centered approach, anomalous sets of nodes can then be identified. A key feature of this approach is its interpretability and explainability, since we can directly assess anomalous nodes in the context of the network topology. We evaluate the proposed method using different datasets, including both synthetic as well as real-world network data. Our results demonstrate the efficacy of the presented approach.

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Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis

Entropy ◽

10.3390/e22090925 ◽

2020 ◽

Vol 22 (9) ◽

pp. 925

Author(s):

Shuo Chen ◽

Zhen Zhang ◽

Chen Mo ◽

Qiong Wu ◽

Peter Kochunov ◽

...

Keyword(s):

Complex Networks ◽

Pattern Analysis ◽

Brain Connectivity ◽

Graph Embedding ◽

Point Pattern Analysis ◽

Point Pattern ◽

Healthy Controls ◽

Weighted Networks ◽

The Difference ◽

Weighted Complex Networks

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.

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