Abstract: Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER.

Abstract: We study the problem of employing social networks for propagate influence when repeat activation is involved. While influence maximization has been extensively studied as the fundamental solution, it neglects the reality that a user may purchase a product/service repeatedly, incurring cumulative sales of the product/service. In this paper, we explore a new problem of cumulative influence maximization that brings the influence maximization a step closer to real-world viral marketing applications. In our problem setting, repeat activation exists and we aim to find a set of initial users, through which the maximal cumulative influence can be stimulated in a given time period. To describe the repeat activation behavior, we adopt the voter model to reflect the variation of activations over time. Under the voter model, we formulate the maximization problem and present an effective algorithm. We test and compare our method with heuristic algorithms on real-world data sets. Experimental results demonstrate the utility of the proposed method.

Abstract: This paper addresses the problem of automatic acquisition of semantic relations between events. Since most of the previous researches rely on annotated corpus, the main challenge is the need for more generic methods to identify related event pairs and to extract event-arguments (particularly the predicate, subject and object). Motivated by this background, we develop a three-phased approach that acquires causality from the Web. Firstly, we use explicit connective markers (such as “because”) as linguistic cues to discover causal related events. Then, we extract the event-arguments based on local dependency parse trees of event expressions. In the final phase, we propose a statistical model to measure the potential causal relations. The present results of our empirical evaluation on a large-scale Chinese Web corpus have shown that (a) the use of local dependency tree extensively improves both the accuracy and recall of event-arguments extraction task; (b) our measure which is an improvement on PMI has a better performance.

Abstract: Social networks have greatly amplified spread of information across different communities. However, we recently observe that various malicious information, such as computer virus and rumors, are broadly spread via social networks. To restrict these malicious information, it is critical to develop effective method to discover the diffusion source nodes in social networks. Many pioneer works have explored the source node identification problem, but they all based on an ideal assumption that there is only a single source node, neglecting the fact that malicious information are often diffused from multiple sources to intentionally avoid network audit. In this paper, we present a multi-source locating method based on a given snapshot of partially and sparsely observed infected nodes in the network. Specifically, we first present a reverse propagation method to detect recovered and unobserved infected nodes in the network, and then we use community cluster algorithms to change the multi-source locating problem into a bunch of single source locating problems. At the last step, we identify the nodes having the largest likelihood estimations as the source node on the infected clusters. Experiments on three different types of complex networks show the performance of the proposed method.

Abstract: Recent work at the borders of network science and control theory have begun to investigate the control of complex systems by studying their underlying network representations. A majority of the work in this nascent field has looked at the number of controls required in order to fully control a network. In this talk I will present research that provides a ready breakdown of this number into categories that are both easy to observe in real world networks as well as instructive in understanding the underlying functional reasons for why the controls must exist. This breakdown is able to shed light on several observations made in the previous literature regarding controllability of networks. This decomposition produces a mechanism to cluster networks into classes that are consistent with their large scale architecture and purpose. Finally, we observe that synthetic models of formation generate networks with control breakdowns substantially different from what is observed in real world networks.