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Cycles in graphs often signify interesting processes. For example, cyclic trading patterns can indicate inefficiencies or economic dependencies in trade networks, cycles in food webs can identify fragile dependencies in ecosystems, and cycles in financial transaction networks can be an indication of money laundering. Identifying such interesting cycles, which can also be constrained to contain a given set of query nodes, although not extensively studied, is thus a problem of considerable importance. In this paper, we introduce the problem of discovering interesting cycles in graphs. We first address the problem of quantifying the extent to which a given cycle is interesting for a particular analyst. We then show that finding cycles according to this interestingness measure is related to the longest cycle and maximum mean-weight cycle problems (in the unconstrained setting) and to the maximum Steiner cycle and maximum mean Steiner cycle problems (in the constrained setting). A complexity analysis shows that finding interesting cycles is NP-hard, and is NP-hard to approximate within a constant factor in the unconstrained setting, and within a factor polynomial in the input size for the constrained setting. The latter inapproximability result implies a similar result for the maximum Steiner cycle and maximum mean Steiner cycle problems. Motivated by these hardness results, we propose a number of efficient heuristic algorithms. We verify the effectiveness of the proposed methods and demonstrate their practical utility on two real-world use cases: a food web and an international trade-network dataset., Accepted for CIKM'19

Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity. Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these. For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures. Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk.

We study the performance of three dynamic programming methods on music retrieval. The methods are designed for time series matching but can be directly applied to retrieval of music. Dynamic Time Warping (DTW) identifies an optimal alignment between two time series, and computes the matching cost corresponding to that alignment. Significant speed-ups can be achieved by constrained Dynamic Time Warping (cDTW), which narrows down the set of positions in one time series that can be matched with specific positions in the other time series. Both methods are designed for full sequence matching but can also be applied for subsequence matching, by using a sliding window over each database sequence to compute a matching score for each database subsequence. In addition, SPRING is a dynamic programming approach designed for subsequence matching, where the query is matched with a database subsequence without requiring the match length to be equal to the query length. SPRING has a lower computational cost than DTW and cDTW. Our database consists of a set of MIDI files taken from the web. Each MIDI file has been converted to a 2-dimensional time series, taking into account both note pitches and durations. We have used synthetic queries of fixed size and different noise levels. Surprisingly, when looking for the top-K best matches, all three approaches show similar behavior in terms of retrieval accuracy for small values of K. This suggests that for the specific application area, a computationally cheaper method, such as SPRING, is sufficient to retrieve the best top-K matches.

Monitoring human motion has recently received great attention and can be used in many applications, such as human motion prediction. We present the collected data set from a body sensor network attached to the human body. The set of sensors consists of accelerometers measuring acceleration in three directions that are attached to the upper and lower back as well as the knees and ankles. In addition, pressures on the insoles are measured with four pressure sensors inside each shoe. Two types of motion are considered: walking backwards on a straight line and walking forwards on a figure-8 path. Finally, we study and present basic statistics of the data.